Fundamentals of Image Processing Lab Manual 2014.pdf

Fundamentals Fundamentals of Image Processing Laboratory Manual

Enrollment No. _______________ Name of the student:_________________________________

Government Engineering College, Rajkot

GOVERNMENT ENGINEERING COLLEGE, RAJKOT

CERTIFICATE

This

is

to

certify

that

Mr/Miss

____________________________________Enrollment No. _____________ of B.E. (E.C.) SEM-VIII has satisfactorily completed the term work of the subject Fundamentals of Image Processing prescribed by Gujarat Technological University during the academic term January-2014 to April-2014.

Date:

Signature of the faculty

SET 1 completed on ________________ SET 2 completed on _________________

Lab Manual of Fundamentals Fundamentals of Image Processing

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GOVERNMENT ENGINEERING COLLEGE, RAJKOT

CERTIFICATE

This

is

to

certify

that

Mr/Miss

____________________________________Enrollment No. _____________ of B.E. (E.C.) SEM-VIII has satisfactorily completed the term work of the subject Fundamentals of Image Processing prescribed by Gujarat Technological University during the academic term January-2014 to April-2014.

Date:

Signature of the faculty

SET 1 completed on ________________ SET 2 completed on _________________


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List of Experiments Sr. No. 1.

2.

Name of Experiment Write program to read and display digital image using MATLAB or SCILAB  Become familiar with SCILAB/MATLAB Basic commands  Read and display image in SCILAB/MATLAB SCILAB/MATLAB  Resize given image  Convert given color image into gray-scale image  Convert given color/gray-scale image into black & white image  Draw image profile  Seprate color image in three R G & B planes  Create color image using R, G and B three separate planes  Flow control and LOOP in SCILAB  Write given 2-D data in image file To write and execute execute image processing processing programs using using point processing method

 Obtain Negative image  Obtain Flip image  Thresholding  Contrast stretching 3.

To write and execute execute programs for image arithmetic operations

 Addition of two images  Subtract one image from other image  Calculate mean value of image  Different Brightness by changing mean value 4.

To write and execute execute programs for image logical operations

     5.

To write a program program for histogram calculation and equalization equalization

    6.

AND operation between two images OR operation between two images Calculate intersection of two images Water Marking using EX-OR operation NOT operation (Negative image) Standard MATLAB function Program without using standard MATLAB functions C Program Use Simulink to plot histogram of colour image

To write and execute execute program for geometric geometric transformation of image

 Translation  Scaling  Rotation  Shrinking  Zooming E.C. Department, Department, Government Engineering College, Rajkot

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7.

To understand various image noise models and to write programs for image restoration

   8.

Write and execute programs to remove noise using spatial filters

  9.

11. 12.

Understand 1-D and 2-D convolution process Use 3x3 Mask for low pass filter and high pass filter

Write and execute programs for image frequency domain filtering

   10.

Remove Salt and Pepper Noise Minimize Gaussian noise Median filter and Weiner filter

Apply FFT on given image Perform low pass and high pass filtering in frequency domain Apply IFFT to reconstruct image

Write a program in C and MATLAB/SCILAB for edge detection using different edge detection mask Write and execute program for image morphological operations erosion and dilation. To write and execute program for wavelet transform on given image and perform inverse wavelet transform to reconstruct image.

Instructions to the students:



Solve exercise given at the end of each practical, write answers and execute it



Write and execute all programs either in MATLAB or SCILAB. You can cut and paste results of image processing in soft copy of this lab manual. Write all the programs in separate folder. Prepare your folder in your laptop/computer.

 

Do not use standard MATLAB functions whenever specified. Write program yourself without using standard functions Get signature of staff regularly after completion of practical and exercise regularly. Show your programs when faculty asks for it.

 There are two SET of experiments. SET 1 having seven experiments. Students must complete first SET of seven experiments before 15 th March 2014 and get signature of faculties in all six experiments. SET 2 having another 5 experiments. Students should complete SET2 before 30 th April 2014.

Lab Manual of Fundamentals of Image Processing

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EXPERIMENT NO. 1 AIM: Write program to read and display digital image using MA TLAB or SCILAB Introduction of SCILAB: SciLab is a programming language for mathematical operations It provides development environment for mathematical programming. It is very much useful for matrix manipulation hence it is good choice for signal processing and image processing applications. It is similar to MATLAB. MATLAB is commercial software while SCILAB is open source software. It has one thousand four hundred in-built functions and it is growing day by day. Image and Video processing toolbox will be useful for this subject. Students can download free SCILAB software from following website: http://www.scilab.in You can also create your log in on the e-Prayog website: http://59.181.142.81 and get scilab link from there. For image processing experiments you need to download SIVP (Speech, Image, Video Processing) Toolbox. Saving and Loading Programs in SCILAB: SCILAB can be used as a prototyping environment in which we try things out, examine the results and then adjust our programs to give better performance. We can give SCILAB commands on command prompt. For large number of commands, we can use SCILAB editor. We can write sequence of commands in SCILAB editor and then save as .sce files. We can execute .sce file to see the execution of the program. Some commands and programs in SCILAB: [1] Declaring matrices: In SciLab, there is no need to declare variables; new variables are simply introduced as they are required. A = [1 2 3; 4 5 6; 7 8 9] The above SCILAB command declares following matrix:

1 A =  4 7

2 5 8

3

  9 6

Keep following things in mind while declaring variables/matrices:  No spaces  Don’t start with a number  Variable names are case sensitive E.C. Department, Government Engineering College, Rajkot

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[2] Populating matrix elements with zeros: SciLab also provides a number of functions which can be used to populate a new matrix with particular values. For example to make a matrix full of zeroes we can use the function zeros(m, n) which creates an m*n matrix of zeros as follows: B = zeros(3,3)

0 B = 0  0

0

0

0

0

0

  0

[2] Knowing size of matrix: Syntax: [rows, cols] = size(A); This command gives size of matrix Above command gives result: rows=3, cols=3 [3] Reading Image file We can use following command to read image file: myImage=imread(‘File name with path’) If name of the image file is test.bmp and if it is in /home/chv folder above commands can be written as: myImage=imread(‘/home/chv/test.bmp’) The image filename can be given as a full file path or as a file path relative to the SciLab current directory. The current directory can be changed from the main SciLab interface window or by cd (change directory command). The supported file formats include ‘bmp’, ‘gif’, ‘jpg’ and ‘png’. After giving above command image data is available in myImage variable. You can use any variable name. [4] Displaying image After reading image data using above function, we can display images in SciLab using imshow function. This function simply takes the array storing the image values as its only parameter. Syntax: imshow() Example: imshow(myImage);


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[5] Knowing size of image in pixels: Size of the image in pixels can be found out by following command: [Rows, Cols] = size(myImage) [6] Image resizing Image resizing can be done by following command: imresize(Image,{Parameters}); For example: Consider that we read the image in variable myImage using imread function than we can resize the image stored in this variable by following command imresize(myImage,[256,256],’nearest’); This command will convert image of any size into image of 256x256 using nearest neighbor technique. [7]Converting Color image into Grayscale image: Color image can be converted into Grayscale image by matlab/scilab function rgb2gray. Example: myGrayImage=rgb2gray(myImage) [8]Converting Color image into Binary Image: Color image can be converted into Binary image using f unction im2bw: Example: myBWImage=im2bw(myImage) Where myImage is 2D image data [9] Intensity profile along line given by two points on image Drawing of intensity profile along single line on given image (Scan one line along the image and draw intensity values (1-D signal)) can be done by function improfile() Example: improfile(myImage,[0,150],[200,150]); If given image is chess-board pattern, profile graph would be squarewave. [10] Separate color image into three separate R,G,B planes If we read given color image using imread() function, we get 3-D matrix. To separate out R,G and B planes, we can read each plane separately as follows:

E.C. Department, Government Engineering College, Rajkot

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Example: myImage=imread(‘RGBBar.bmp’); RED=myImage(:,:,1); GREEN=myImage(:,:,2); BLUE=myImage(:,:,3); [11] Combine three separate R,G,B planes to create color image If we have three R,G and B planes separately and if we want to create color image from it, we can use concatenate function: Example: newImage=cat(3,RED,BLUE,GREEN)

[12] Flow control in SCILAB If statements are simply used to make decisions in SciLab Syntax: if then else end If we wish to perform thresholding of an image we could use following statements in SCILAB Program: if ImageData(r, c) > threshold then ThresholdedImage(r, c) = 255; else ThresholdedImage(r, c) = 0; end

[13] Loops in SCILAB: There is for loop and while loop in SCILAB. While loop While loop repeat a piece of work as long as a condition holds true. The while loop in SciLab uses the following syntax: while end For loop in SCILAB is very popular for image processing because it is particularly useful for iterating through the members of a matrix. The SCILAB for loop uses the following syntax: Lab Manual of Fundamentals of Image Processing

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for index = : end Example: Following SCILAB code iterates through each pixel of image, does summation of pixel values, finds average pixel value which represents average brightness of the scene. myImage=imread(‘/home/chv/test.bmp’) [Rows, Cols] = size(myImage) total = 0; for rowIndex = 1:Rows for colIndex = 1:Cols total = total + ImageData(rowIndex, colIndex); end end average=total/(Rows*Cols) [14] Saving two dimensional matrix in Image file We read image, image data is available in two dimensional matrix. We process the matrix. After processing, we need to save the processed data in form of image. To save image data in image file, following command is used. imwrite(ImageData, ' Testout.bmp', 'bmp'); This command writes ImageData in file “Testout.bmp”. It also supports ‘gif’, ‘jpg’ and ‘png’ file formats.

:: WORKSHEET :: [1] Give following commands in SCILAB, observe output and write explanation of each command t = 0:0.01:1 y=sin(2*%pi*10) plot(y)

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ E.C. Department, Government Engineering College, Rajkot

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_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [2] Create image of size 512x512 black square using monochrome, 256 gray-level using paint or any other relevant software and save it file name “black.bmp” Read and display image using SCILAB/MATLAB commands

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [3] Execute following program for the image created in exercise[2] and observe the result. Write comments on the result :

i mgDat a=i mr ead( ' bl ack. bmp' ) ; [ Rows, Col s] =si ze( i mgDat a) ; i mshow( i mgDat a) ; f or i =1: Rows f or j =1: Col s i f ( i >Rows/ 4 && i <3*Rows/ 4) i f ( j >Col s/ 4 && j <3*Col s/ 4) i mgDat a( i , j ) =0; end end end end f i gur e; i mshow( i mgDat a) ;


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Write your comments on the result after executing the program:

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [4] Write MATLAB command for the following

 Read color image ‘RGBBar.bmp’ given in working directory, convert it into Grey-scale image

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________  Read color image ‘RGBBar.bmp’ given in working directory, Find size of the image, Resize it to 256x256

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________


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EXPERIMENT NO. 2 AIM:

To write and execute image processing programs using point processing methods  Obtain Negative image  Obtain Flip image  Threshold operation (Thresholding)  Contrast stretching

Introduction: Image enhancement can be done in two domain: [1] Spatial Domain and [2] Transform domain. In Spatial domain Image processing, there are two ways:  Point processing (Single pixel is processed at a time)  Neighborhood processing (Mask processing) Convolution of 3x3 or 5x5 or other size of mask with image In point processing, Grey level transformation can be given by following equation  g(x,y)=T(f(x,y))  s=T(r) Where, f(x,y) is original image, g(x,y) is transformed image s = gray level of transformed image r = gray level of original image Threshold operation: In threshold operation, Pixel value greater than threshold value is made white and pixel value less than threshold value is made black. If we consider image having gray levels r and if grey levels in output image is s than,

=0

if r ≤ m

= 255

if r > m

s

s

Where m = threshold


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MATLAB Program for Thresholding: % Exper i ment No. 2 Thr eshol di ng ( Ext r eme cont r ast st r et chi ng) % Scan any document and use t hi s met hod t o make i t cl ean % Exampl e: scan your own si gnat ur e and make i t cl ean wi t h t hr eshol di ng f i l ename=i nput ( ' Ent er f i l e name: ' , ' s' ) ; y=i mr ead( f i l ename) ; T=i nput ( ' Ent er t hr eshol d val ue bet ween 0 t o 255: ' ) ; i f ( ndi ms( y) ==3) y=r gb2gr ay( y) ; end [ m, n] =si ze( y) ; i mshow( y) ; f or i =1: m f or j =1: n i f ( y( i , j ) >T) y( i , j ) =255; el s e y( i , j ) =0; end end end f i gur e; i mshow( y) ; Run above program for different threshold values and find out optimum threshold value for which you are getting better result. Horizontal flipping: % Exper i ment 2: Fl i p gi ven i mage hor i zont al l y % Read i n an i mage f i l ename=i nput ( ' Ent er f i l e name: ' , ' s' ) ; i magedat a = i mr ead( f i l ename) ; i f ( ndi ms( i magedat a) ==3) i magedat a=r gb2gr ay( i magedat a) ; end %Det er mi ne t he si ze of t he i mage [ r ows, col s] = si ze( i magedat a) ;

%Decl ar e a new mat r i x t o st or e t he newl y cr eat ed f l i pped i mage Fl i ppedI mageDat a = zer os( r ows, col s) ; %Gener at e t he f l i pped i mage f or r = 1: r ows f or c = 1: col s Fl i ppedI mageDat a( r , col s+1- c) = i magedat a( r , c) ; end end %Di spl ay t he or i gi nal i mage and f l i pped i mage E.C. Department, Government Engineering College, Rajkot

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subpl ot ( 2, 1, 1) ; i mshow( i magedat a) ; subpl ot ( 2, 1, 2) ; i mshow( Fl i ppedI mageDat a, [ ] ) ; %Wr i t e f l i pped i mage t o a f i l e i mwr i t e( mat 2gr ay( Fl i ppedI mageDat a) , ' Fl i pTes t . j pg' , ' qual i t y' , 99) ;

Negative Image: Negative Image can be obtained by subtracting each pixel value from 255. 255

= 255-r

r

255

MATLAB Program to obtain Negative Image: % Exper i ment No. 2 % To obt ai n Negat i ve I mage % I mage Pr ocess i ng Lab, GEC Raj kot cl c; cl ose; %Or i gi nal I mage [ namef i l e, pat hname] =ui get f i l e( {' *. bmp; *. t i f ; *. j pg; *. gi f ' , ' I MAG E Fi l es ( * . bmp, * . t i f , * . j pg, * . gi f ) ' }, ' Choose Gr ayScal e I mage' ) ; i mg=i mr ead( st r cat ( pat hname, namef i l e) ) ; i f ( si ze( i mg, 3) ==3) i mg=r gb2gr ay( i mg) ; end

[ r ows, col s] =si ze( i mg) ; neg_i mg=zer os( r ows, col s) ; f or r = 1: r ows f or c = 1: col s neg_i mg( r , c) = 255- i mg( r , c) ; end end %Di spl ay or i gi nal and negat i ve i mages subpl ot ( 2, 1, 1) ; i mshow( i mg) ; subpl ot ( 2, 1, 2) ; i mshow( neg_i mg, [ ] ) ;


%Cal cul at e negat i ve i mage

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Contrast Stretching: Contrast Stretching means Darkening level below threshold value m and whitening level above threshold value m. This technique will enhance contrast of given image. Thresholding is example of extreme constrast stretching. Practically contrast stretching is done by piecewise linear approximation of graph shown in left by following equations:

= x.r

0 ≤ r
= y.r

a ≤ r
= z.r

b ≤ r<255

s s s

Where x,y and z are different slopes a

b

RED line shows iecewise a

roximation

MATLAB Program for contrast stretching: % Exper i ment No. 2 Cont r ast st r et chi ng usi ng t hr ee sl opes % and t wo t hr eshol d val ues a & b cl c; c l ear al l ; [ namef i l e, pat hname] =ui get f i l e( {' *. bmp; *. t i f ; *. t i f f ; *. j pg; *. j pe g; *. gi f ' , ' I MAGE Fi l es ( * . bmp, *. t i f , *. t i f f , *. j pg, *. j peg, *. gi f ) ' }, ' Chose Gr ayScal e I mage' ) ; i mg=i mr ead( st r cat ( pat hname, namef i l e) ) ; i f ( si ze( i mg, 3) ==3) i mg=r gb2gr ay( i mg) ; end [ r ow, col ] =si ze( i mg) ; newi mg=zer os( r ow, col ) ; a=i nput ( ' Ent er t hr eshol d val ue a: ' ) ; b=i nput ( ' Ent er t hr eshol d val ue b: ' ) ; f or i =1: r ow f or j =1: col i f ( i mg( i , j ) <=a) newi mg( i , j ) =i mg( i , j ) ; end i f ( i mg( i , j ) >a && i mg( i , j ) <=b) newi mg( i , j ) =2*i mg( i , j ) ; end i f ( i mg( i , j ) >b) newi mg( i , j ) =i mg( i , j ) ; end end end subpl ot ( 2, 1, 1) ; i mshow( ui nt 8( i mg) ) ; subpl ot ( 2, 1, 2) ; i mshow( ui nt 8( newi mg) ) ; E.C. Department, Government Engineering College, Rajkot

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:: WORKSHEET :: [1] Modify program of horizontal flipping for getting vertical flipping. Apply it on some image

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [1] In contrast stretching program take threshold values 50 and 100, Use slope 3 for gray levels between 0 to 50, slope 2 for gray levels between 50 to 100 and slope 1 for rest gray levels. Modify and write program again. Execute it for some image.

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Lab Manual of Fundamentals of Image Processing

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_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Copy and Paste original and contrast stretched images here:


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EXPERIMENT NO. 3 AIM: To write and execute programs for image arithmetic operations Introduction: Arithmetic operations like image addition, subtraction, multiplication and division is possible. If there is two images I 1 and I2 then addition of image can be given by: I(x,y) = I1(x,y) + I2(x,y) Where I(x,y) is resultant image due to addition of two images. x and y are coordinates of image. Image addition is pixel to pixel. Value of pixel should not cross maximum allowed value that is 255 for 8 bit grey scale image. When it exceeds value 255, it should be clipped to 255. To increase overall brightness of the image, we can add some constant value depending on brightness required. In example program we will add value 50 to the image and compare brightness of original and modified image. To decrease brightness of image, we can subtract constant value. In example program, value 100 is subtracted for every pixel. Care should be taken that pixel value should not less than 0 (minus value). Subtract operation can be used to obtain complement (negative) image in which every pixel is subtracted from maximum value i.e. 255 for 8 bit greyscale image. Multiplication operation can be used to mask the image for obtaining region of interest. Black and white mask (binary image) containing 1s and 0s is multiplied with image to get resultant image. To obtain binary image function im2bw() can be used. Multiplication operation can also be used to increase brightness of the image Division operation results into floating point numbers. So data type required is floating point numbers. It can be converted into integer data type while storing the image. Division can be used to decrease the brightness of the image. It can also used to detect change in image. Execute following MATLAB program: %Experiment No. 3 Image Arithmetic Operations %Image Processing Lab, E.C. Department, GEC Rajkot %Image addition is done between two similar size of image, so image resize %function is used to make size of both image same. %I=I1+I2 clc close all I1 = imread('cameraman.tif'); I2 = imread('rice.png'); Lab Manual of Fundamentals of Image Processing

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subplot(2, 2, 1);imshow(I1);title('Original image I1'); subplot(2, 2, 2);imshow(I2);title('Original image I2'); I=I1+I2; % Addition of two images subplot(2, 2, 3);imshow(I);title('Addition of image I1+I2'); I=I1-I2; %Subtraction of two images subplot(2, 2, 4);imshow(I);title('Subtraction of image I1-I2'); figure; subplot(2, 2, 1);imshow(I1);title('Original image I1'); I=I1+50; subplot(2, 2, 2);imshow(I);title('Bright Image I'); I=I1-100; subplot(2, 2, 3);imshow(I);title('Dark Image I'); M=imread('Mask.tif'); M=im2bw(M) % Converts into binary image having 0s and 1s I=uint8(I1).*uint8(M); %Type casting before multiplication subplot(2, 2, 4);imshow(I);title('Masked Image I'); Result after execution of program: Original image I1

Original image I2

Addition of image I1+I2

Subtraction of image I1-I2


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Original image I1

Bright Image I

Dark Image I

Masked Image I

Display image with different brightness: cl ose al l ; c l ear al l ; [ f i l ename, pat hname] =ui get f i l e( {' * . bmp; * . j I mage Fi l e' }) ; myi mage=i mr ead( f i l ename) ; i f ( si ze( myi mage, 3) ==3) myi mage =r gb2gr ay( myi mage) ; end [ Rows, Col s] = si ze( myi mage) newi mage=zer os( Rows, Col s) ; k=1; whi l e k<5, f or i = 1: Rows f or j = 1: Col s i f k==1 newi mage( i , j ) =myi mage( i , j end i f k==2 newi mage( i , j ) =myi mage( i , j end i f k==3 newi mage( i , j ) =myi mage( i , j end i f k==4 newi mage( i , j ) =myi mage( i , j Lab Manual of Fundamentals of Image Processing

pg; * . gi f ' , ' Choose

) - 100; ) - 50; ) +50; ) +50; Page 20

end end end subpl ot ( 2, 2, k) ; i mshow( newi mage, [ ] ) ; k=k+1; end

Result:

Calculate mean value: Mean value can be calculated by MATLAB function mean() Alternately following code can be used to calculate mean value:

[ Rows, Col s] = si ze( myi mage) newi mage=zer os( Rows, Col s) ; t ot al = 0; f or i = 1: Rows f or j = 1: Col s t ot al = t ot al + myi mage( i , j ) ; end end aver age=t ot al / ( Rows* Col s)


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:: WORKSHEET :: [1] In above program, use functions imadd() functions to addition, imsubtract() for subtraction immultiply() for multiplication operations. Use imcomplement() function to get complement of image. Write program again using these functions in following space.

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [2] Write Program to read any image, resize it to 256x256. Apply square mask shown in following figure so that only middle part of the image is visible.

128

256

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Lab Manual of Fundamentals of Image Processing

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_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [3] Write your own matlab function addbrightness() and use it to increase brightness of given image.

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ E.C. Department, Government Engineering College, Rajkot

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EXPERIMENT NO. 4 AIM: To write and execute programs for image logical operations Introduction: Bitwise logical operations can be performed between pixels of one or more than one image. AND/NAND Logical operations can be used for following applications:  Compute intersection of the images  Design of filter masks  Slicing of gray scale images OR/NOR logical operations can be used for following applications:  Merging of two images XOR/XNOR operations can be used for following applications:  To detect change in gray level in the image  Check similarity of two images NOT operation is used for:  To obtain negative image  Making some features clear Program: Experiment No. 4 Image Logical Operations %Image Processing Lab, E.C. Department, GEC Rajkot %Logical operations between two image circle and pentagon is shown clc close all clc close all myimageA=imread( 'circle.jpg'); myimageA=rgb2gray(myimageA); myimageB=imread('pentagon.jpg'); myimageB=rgb2gray(myimageB); subplot(3,2,1) imshow(myimageA),title('Image A '); % Display the Original Image B subplot(3,2,2) imshow(myimageB),title('Image B'); % Take a complement of Image A and Display it subplot(3,2,3) cimageA= ~myimageA ; imshow(cimageA), title('Complement of Image A'); % Take a Ex-OR of Image A and Image B and Display it subplot(3,2,4) xorimage= xor(myimageA,myimageB); imshow(xorimage), title('Image A XOR Image B');


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% Take OR of Image A and Image B and Display it subplot(3,2,5) orimage= myimageA | myimageB; imshow(orimage), title('Image A OR Image B '); % Take AND of Image A and Image B and Display it subplot(3,2,6) andimage= myimageA & myimageB; imshow(andimage), title('Image A AND Image B ');

Result after execution of program: Image A

Image B

Complement of Image A

Image A XOR Image B

Image A OR Image B

Image A AND Image B

Watermarking using EX-OR operation: To provide copy protection of digital audio, image and video two techniques are used: encryption and watermarking. Encryption techniques normally used to protect data during transmission from sender to receiver. Once data received at receiver, it is decrypted and data is same as original which is not protected. Watermarking techniques can compliment encryption by embedding secret key into original data. Watermarking can be applied to audio, image or video data. Watermarking technique used for can be visible or non-visible. For visible watermarking technique, watermark image is visible on original image in E.C. Department, Government Engineering College, Rajkot

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light form. In non-visible watermarking technique, watermark image is hidden inside original image. In digital watermarking, watermarking key bits are scattered in the image and cannot be identified. There are so many techniques being developed for secure and robust watermarking. Watermarking using EX-OR operation is simplest technique. In following program, Ex-OR operation is performed between original image data and watermarking key. To extract watermarking image, EX-OR operation is performed again between original image and watermarking image. In this message, message GEC RAJKOT is watermarked in the digital image in invisible form. Program: % Experiment No. 4 EX-OR operation to show watermarking application % Image Processing Lab, GEC Rajkot clc; close; %Original Image [namefile,pathname]=uigetfile({ '*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)' },'Chose GrayScale Image'); img=imread(strcat(pathname,namefile)); if (size(img,3)==3) img=rgb2gray(img); end subplot(2,2,1); imshow(img) title('Original Image'); [p q] = size(img); key = imread('KEY_GEC_RAJKOT.bmp' ); key = imresize(key,[p q]); subplot(2,2,2); imshow(key); title('Water marking key'); w_img=bitxor(uint8(img),uint8(key)); subplot(2,2,3); imshow(w_img);title('Watermarked image'); extracted_key=bitxor(uint8(w_img),uint8(img)); subplot(2,2,4); imshow(double(extracted_key)); title('Extracted Key'); Result:


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Original Image

Water marking key

Watermarked image

Extracted Key

:: WORKSHEET :: [1] Prepare any two images of size 256x256 in paint. Save it in JPEG format 256 gray levels. Perform logical NOR, NAND operations between two images. Write program and paste your results.

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ E.C. Department, Government Engineering College, Rajkot

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Result after execution of program: (Copy and paste original and processed image here)


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EXPERIMENT NO. 5 AIM: To write a program for histogram calculation and equalization

   

Standard MATLAB function Program without using standard MATLAB functions C Program Use Simulink to plot histogram of colour image

Introduction:  Histogram is bar-graph used to profile the occurrence of each gray level in the image  Mathematically it is represented as h(rk)=nk Where rk is kth grey level and n k is number of pixel having that grey level  Histogram can tell us whether image was scanned properly or not. It gives us idea about tonal distribution in the i mage.  Histogram equalization can be applied to improve appearance of the image 



Histogram also tells us about objects in the image. Object in an image have similar gray levels so histogram helps us to select threshold value for object detection. Histogram can be used for image segmentation.

Pseudo code: Calculate histogram: Loop over ROWS of the image Loop over COLS of then image Pixel value hist(k)=hist(k)+1 End COLS loop End ROWS loop

i = 1 to rows j = 1 to cols k=data(i,j)

Calculate sum of the hist: Loop over gray levels i = 0 to no. of gray levels sum=sum+hist(i); sum_of_hist(i)=sum; End loop Area of image = rows*cols Dm=Number of gray levels in the output image Loop over ROWS Loop over COLS k = data(i,j) data(i,j) = (Dm/area)*sum_of_hist(k) End COLS oop End ROWS Loop E.C. Department, Government Engineering College, Rajkot

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Standard MATLAB function for histogram and histogram equalization: [1] imhist function It computes histogram of given image and plot Example: myimage=imread(‘tier.jpg’) imhist(myimage); [1] histeq function It computes histogram and equalize it. Example: myimage = imread(‘rice.bmp’); newimage= histeq(myimage);

Sample program using standard functions:

cl ose al l ; c l ear al l ; [ f i l ename, pat hname] =ui get f i l e( {' * . bmp; * . j pg; * . gi f ' , ' Choose Poor l y s canned I mage' }) ; myi mage=i mr ead( f i l ename) ; i f ( si ze( myi mage, 3) ==3) myi mage =r gb2gr ay( myi mage) ; end i mhi st ( myi mage) ; newi mage= hi st eq( myi mage) ; f i gur e; subpl ot ( 2, 2, 1) ; i mshow( myi mage) ; t i t l e( ' Or i gi nal i mage' ) ; subpl ot ( 2, 2, 2) ; i mshow( newi mage) ; t i t l e( ' Hi st ogr am equal i zed mage' ) ;

Sample Program in MATLAB (without using standard function): % Experiment No. 5 %Program for calculation and equalisation of the histogram % Image Processing Lab, EC Department, GEC Rajkot close all; clear all; [ f i l ename, pat hname] =ui get f i l e( {' * . bmp; * . j pg; * . gi f ' , ' Choose Poor l y s canned I mage' }) ; data=imread(filename); if (size(data,3)==3) data=rgb2gray(data); end


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subplot(2,2,1);imshow(data); title('Original image'); [rows cols]=size(data) myhist=zeros(1,256); % Calculation of histogram for i=1:rows for j=1:cols m=double(data(i,j)); myhist(m+1)=myhist(m+1)+1; end end subplot(2,2,2);bar(myhist); title('Histogram of original image'); sum=0; %Cumulative values for i=0:255 sum=sum+myhist(i+1); sum_of_hist(i+1)=sum; end area=rows*cols; %Dm=input('Enter no. of gray levels in output image: '); Dm=256; for i=1:rows for j=1:cols n=double(data(i,j)); data(i,j)=sum_of_hist(n+1)*Dm/area; end end %Calculation of histogram for equalised image for i=1:rows for j=1:cols m=double(data(i,j)); myhist(m+1)=myhist(m+1)+1; end end subplot(2,2,3);bar(myhist);title('Equalised Histogram'); subplot(2,2,4);imshow(data); title('Image after histogram equalisation');


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Program output: Original image

Histogram of original image 1500

1000

500

0

Equalised Histogram

0

100

200

300

Image after histogram equalisation

3000

2000

1000

0

0

100

200

300

Sample Program in C language: Requirements:  Add member function histogram() in Image class for histogram calculation and equalisation  Add member function print_histogram() in Image class to display histogram of an image.  Implement this member function in implementation program  You can use 1010 sample image to observe results

void image::histogram(char filename[]) { int i,j,m; FILE *f1; int sum_of_hist[100]; f1=fopen(filename,"rb"); int k=rows*cols; for(i=0; i
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for(j=1; j\n"); printf(" "); for(i=0;i<=25;i++)printf("_"); printf("\n"); for(i=0; i<16; i++) { E.C. Department, Government Engineering College, Rajkot

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printf("%2d |",i); for(j=1; j<=hist[i];j++) printf("*"); printf("\n"); } printf("\n\n"); }

Using Simulink to plot histogram of image: Simulink is very much user friendly if you are convergent with image processing operations.  Type simulink command on the command prompt  Click on Computer Vision System toolbox  There are various options available in this tool box, go through all the options one by one.  We will use following blocks in this experiment …. [1] Computer Vision System Toolbox > Sources>Image from file [2] Computer Vision System Toolbox > Sinks>Video Viewer [3] Simulink > Math Operations > Matrix Concatenate [4] Computer Vision System Toolbox > Statistics>Histogram [5] DSP System Toolbox > Signal Processing Sinks > Vector Scope Arrange all blocks as per following diagram and connect them.

R Video Viewer

G B

Video Viewer

Histogram

R Teddy.jpg

G B

2 Histogram1

Image From File

Matrix Concatenate

User Vector Scope

Histogram2


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Select Image file by clicking on image block. Set following parameters in image block:

   

Sample time = inf Image signal = Separate color signals Output port labels: = R|G|B Output data type: = double

Set following parameters in histogram block:  Lower limit of histogram: 0  Upper limit of histogram: 1  Number of bins: = 256 Set following parameters in matrix concatenate block:  Number of input parameters: 3 Set following parameters in Vector scope  Scope Properties pane, Input domain = User-defined  Display Properties pane, clear the Frame number check box  Display Properties pane, select the Channel legend check box  Display Properties pane, select the Compact display check box  Axis Properties pane, clear the Inherit sample increment from input check box.  Axis Properties pane, Minimum Y-limit = 0  Axis Properties pane, Maximum Y-limit = 1  Axis Properties pane, Y-axis label = Count  Line Properties pane, Line markers = .|s|d  Line Properties pane, Line colors = [1 0 0]|[0 1 0]|[0 0 1]

Open the Configuration dialog box by selecting Configuration Parameters from the Simulation menu. Set the parameters as follows:  Solver pane, Stop time = 0  Solver pane, Type = Fixed-step  Solver pane, Solver = Discrete (no continuous states) Run simulation and see result. Change file and do experiment again.


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:: WORKSHEET :: [1] Take your own photograph in dark area. Improve its appearance using histogram equalization technique. (Copy and paste your original image, its histogram, equalized image and its histogram)


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EXPERIMENT NO. 6 AIM: To write and execute program for geometric transformation of image Introduction: We will perform following geometric transformations on the image in this experiment Translation: Translation is movement of image to new position. Mathematically translation is represented as: x’ = x + x

and y’ = y + y

In matrix form translation is represented by:

 x'

y'

1  1 = 0   0

   y  1 

0

 x

1 0

 x   y     1 

Scaling: Scaling means enlarging or shrinking. Mathematically scaling can be represented by: x’ = x × Sx

and y’ = y × Sy

In matrix form scaling is represented by:

 x'

y'

 S x  1 = 0   0

0 Sy 0

0   x 

  1 

 y     1 

0

Rotation: Image can be rotated by an angle , in matrix form it can be represented as: x’ = xcos - ysin and y’ = xsin + ycos In matrix form rotation is represented by:

 x'

y'

 cos   1 = sin    0

 sin cos

 

0

0   x 

  1  0

 y     1 

If  is substituted with -, this matrix rotates the image in clockwise direction. Shearing: Image can be distorted (sheared) either in x direction or y direction. Shearing can be represented as:

x’ = shx × y

y’ = y

In matrix form rotation is represented by:


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 1  Xshear= sh x   0

0 1 0

0   x 

  1  0

 y     1 

Shearing in Y direction can be given by:

x’ = x

y’ = y × shy

1  Yshear=  0  0

sh y

1 0

0   x 

  1  0

 y     1 

Zooming : zooming of image can be done by process called pixel replication or interpolation. Linear interpolation or some non -linear interpolation like cubic interpolation can be performed for better result. Program: %Experiment No. 4 Image Geometric transformations %Image Processing Lab, E.C. Department, GEC Rajkot %To rotate given image using standard Matlab function imrotate() %To transform image using standard Matlab function imtransform() % using shearing matrix. clc close all filename=input('Enter File Name :','s'); x=imread(filename); x=rgb2gray(x); subplot(2,2,1); imshow(x); title('Orignial Image'); y=imrotate(x,45,'bilinear','crop'); subplot(2,2,2); imshow(y); title('Image rotated by 45 degree'); y=imrotate(x,90,'bilinear','crop'); subplot(2,2,3); imshow(y); title('Image rotated by 90 degree'); y=imrotate(x,-45,'bilinear','crop'); subplot(2,2,4); imshow(y); title('Image rotated by -45 degree'); x = imread('cameraman.tif'); tform = maketform('affine',[1 0 0; .5 1 0; 0 0 1]); y = imtransform(x,tform); figure; subplot(2,2,1); imshow(x); title('Orignial Image'); subplot(2,2,2); imshow(y); title('Shear in X direction'); tform = maketform('affine',[1 0.5 0; 0 1 0; 0 0 1]); y = imtransform(x,tform); subplot(2,2,3); imshow(y); title('Shear in Y direction'); tform = maketform('affine',[1 0.5 0; 0.5 1 0; 0 0 1]); Lab Manual of Fundamentals of Image Processing

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y = imtransform(x,tform); subplot(2,2,4); imshow(y); title('Shear in X-Y direction'); Result after execution of program: Orignial Image

Image rotated by 45 degree

Image rotated by 90 degree

Image rotated by -45 degree

Result after execution of program:

Orignial Image

Shear in Y direction

Shear in X direction

Shear in X-Y direction


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Exercise: [1] In above program, modify matrix for geometric transformation and use imtransform() function for modified matrix. Show the results and your conclusions. Program:

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Result after execution of program:

Conclusion:


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EXPERIMENT NO. 7 AIM: To understand various image noise models and to write programs for image restoration Introduction: Image restoration is the process of removing or minimizing known degradations in the given image. No imaging system gives perfect quality of recorded images due to various reasons. Image restoration is used to improve quality of image by various methods in which it will try to decrease degradations & noise. Degradation of images can occur due to many reasons. Some of them are as under:  Poor Image sensors  Defects of optical lenses in camera  Non-linearity of the electro-optical sensor;  Graininess of the film material which is utilised to store the image  Relative motion between an object and camera  Wrong focus of camera  Atmospheric turbulence in remote sensing or astronomy  Degradation due to temperature sensitive sensors like CCD  Poor light levels Degradation of images causes:  Radiometric degradations;  Geometric distortions;  Spatial degradations. Model of Image Degradation/restoration Process is shown in the following figure.

Spatial domain:

g(x,y)=h(x,y)*f(x,y) + (x,y)

Frequency domain: G(u,v)=H(u,v)F(u,v)+N(u,v)

G(x,y) is spatial domain representation of degraded image and G(u,v) is frequency domain representation of degraded image. Image restoration applies different restoration filters to reconstruct image to remove or minimize degradations.


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Program: % Experiment No. 7 % To add noise in the image and apply image restoration % technique using Wiener filter and median filter % EC Department, GEC Rajkot clear all; close all; [filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose Image File'); A= imread(cat(2,pathname,filename)); if (size(A,3)==3) A=rgb2gray(A); end subplot(2,2,1); imshow(A);title('Original image'); % Add salt & pepper noise B = imnoise(A,'salt & pepper', 0.02); subplot(2,2,2); imshow(B);title('Image with salt & pepper noise'); % Remove Salt & pepper noise by median filters K = medfilt2(B); subplot(2,2,3); imshow(uint8(K)); title('Remove salt & pepper noise by median filter'); % Remove salt & pepper noise by Wiener filter L = wiener2(B,[5 5]); subplot(2,2,4); imshow(uint8(L)); title('Remove salt & pepper noise by Wiener filter'); figure; subplot(2,2,1); imshow(A);title('Original image'); % Add gaussian noise M = imnoise(A,'gaussian',0,0.005); subplot(2,2,2); imshow(M); title('Image with gaussian noise'); % Remove Gaussian noise by Wiener filter L = wiener2(M,[5 5]); subplot(2,2,3); imshow(uint8(L));title('Remove Gaussian noise by Wiener filter'); K = medfilt2(M); subplot(2,2,4); imshow(uint8(K)); title('Remove Gaussian noise by median filter');

Exercise: [1] Draw conclusion from two figures in this experiment. Which filter is

better to remove salt and pepper noise ?

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Lab Manual of Fundamentals of Image Processing

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[2] Explain algorithm used in Median filter with example

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [3] Write mathematical expression for arithmetic mean filter, geometric mean filter, harmonic mean filter and contra-harmonic mean filter

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [3] What is the basic idea behind adaptive filters?

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ E.C. Department, Government Engineering College, Rajkot

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EXPERIMENT NO. 8 AIM: Write and execute programs to remove noise using spatial filters  Understand 1-D and 2-D convolution process  Use 3x3 Mask for low pass filter and high pass filter Introduction:

Spatial Filtering is sometimes also known as neighborhood processing. Neighborhood processing is an appropriate name because you define a center point and perform an operation (or apply a filter) to only those pixels in predetermined neighborhood of that center point. The result of the operation is one value, which becomes the value at the center point's location in the modified image. Each point in the image is processed with its neighbors. The general idea is shown below as a "sliding filter" that moves throughout the image to calculate the value at the center location. In spatial filtering, we perform convolution of data with filter coefficients. In image processing, we perform convolution of 3x3 filter coefficients with 2-D image data. In signal processing, we perform convolution of 1-D data with set of filter coefficients. Program for 1D convolution (Useful for 1-D Signal Processing): clear; clc; x=input("Enter value of x: ") y=input("Enter value of y: ") n=length(x) k=length(y) for z=n+1:n+k-1 x(z)=0; end for u=k+1:n+k-1 y(u)=0; end for i=1:n+k-1 s(i)=0 for j=1:i s(i)=(x(j)*y(i-j+1))+s(i) end end subplot(3,1,1) plot2d3(x) Lab Manual of Fundamentals of Image Processing

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subplot(3,1,2) plot2d3(y) subplot(3,1,3) plot2d3(s) Take value of x: [ 1 2 3 4 5 6 7 8 9 0 10 11 12 13 14 15] Y: [-1 1] Observe result of convolution and write your comments:

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Program for 2D convolution: clear; clc; x=input("Enter value of x in matrix form: ") y=input("Enter value of y in matrix form: ") [ xr ows, xcol s] =si ze( x) ; [ yr ows, ycol s] =si ze( y) ; r esul t =zer os( xr ows+yr ows, xcol s+ycol s) f or r = 1: xr ows- 1 f or c = 1: xcol s- 1 sum=0; f or a=0: yr ows f or b=0: ycol s sum=sum+x( r +a, c+b) *y( a+1, b+1) ; end end E.C. Department, Government Engineering College, Rajkot

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r esul t ( r , c) =sum; end end Enter following 2D matrix for x: 10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

10 10 10 10

Enter following 2D matrix for y: 1 1 1 0

0

0

1

1

1

Observe output values and write your comments

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________


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Program: % Experiment No. 8 Spatial filtering using standard MATLAB function % To apply spatial filters on given image % EC Department, GEC Rajkot clc; close all; clear all; %Define spatial filter masks L1=[1 1 1;1 1 1;1 1 1]; L2=[0 1 0;1 2 1;0 1 0]; L3=[1 2 1;2 4 2;1 2 1]; H1=[-1 -1 -1;-1 9 -1;-1 -1 -1]; H2=[0 -1 0;-1 5 -1;-0 -1 0]; H3=[1 -2 1;-2 5 -2;1 -2 1]; % Read the test image and display it [filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose Image File'); myimage = imread(cat(2,pathname,filename)); if (size(myimage,3)==3) myimage=rgb2gray(myimage); end subplot(3,2,1); imshow(myimage); title('Original Image'); L1 = L1/sum(L1); filt_image= conv2(double(myimage),double(L1)); subplot(3,2,2); imshow(filt_image,[]); title('Filtered image with mask L1'); L2 = L2/sum(L2); filt_image= conv2(double(myimage),double(L2)); subplot(3,2,3); imshow(filt_image,[]); title('Filtered image with mask L2'); L3 = L3/sum(L3); filt_image= conv2(double(myimage),double(L3)); subplot(3,2,4); imshow(filt_image,[]); title('Filtered image with mask L3'); filt_image= conv2(double(myimage),H1); subplot(3,2,5); imshow(filt_image,[]); title('Filtered image with mask H1'); E.C. Department, Government Engineering College, Rajkot

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filt_image= conv2(double(myimage),H2); subplot(3,2,6); imshow(filt_image,[]); title('Filtered image with mask H1'); figure; subplot(2,2,1); imshow(myimage); title('Original Image'); % The command fspecial() is used to create mask % The command imfilter() is used to apply the gaus sian filter mask to the image % Create a Gaussian low pass filter of size 3 gaussmask = fspecial('gaussian',3); filtimg = imfilter(myimage,gaussmask); subplot(2,2,2); imshow(filtimg,[]),title('Output of Gaussian filter 3 X 3' ); % Generate a lowpass filter of size 7 X 7 % The command conv2 is used the apply the filter % This is another way of using the filter avgfilt = [ 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1]; avgfiltmask = avgfilt/sum(avgfilt); convimage= conv2(double(myimage),double(avgfiltmask)); subplot(2,2,3); imshow(convimage,[]); title('Average filter with conv2()'); filt_image= conv2(double(myimage),H3); subplot(3,2,6); imshow(filt_image,[]); title('Filtered image with mask H3');


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:: WORKSHEET :: [1] Write mathematical expression of spatial filtering of image f(x,y) of size MN using mask W of size a b .

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [2] What is need for padding? What is zero padding? Why it is required?

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [3] What is the effect of increasing size of mask?

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ E.C. Department, Government Engineering College, Rajkot

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EXPERIMENT NO. 9 AIM: Write and execute programs for image frequency domain filtering Introduction: In spatial domain, we perform convolution of filter mask with image data. In frequency domain we perform multiplication of Fourier transform of image data with filter transfer function. Fourier transform of image f(x,y) of size MxN can be given by:

Where, u = 0,1,2 ……. M-1 and v = 0,1,2……N-1 Inverse Fourier transform is given by:

Where, x = 0,1,2 ……. M-1 and y = 0,1,2……N-1

Basic steps for filtering in frequency domain:

 Pre-processing: Multiply input image f(x,y) by (-1) x+y to center the     

transform Computer Discrete Fourier Transform F(u,v) of input image f(x,y) Multiply F(u,v) by filter function H(u,v) Result: H(u,v)F(u,v) Computer inverse DFT of the result Obtain real part of the result Post-Processing: Multiply the result by (-1) x+y


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Program: % Experiment 9 Program for frequency domain filtering % EC Department, GEC Rajkot clc; close all; clear all; % Read the image, resize it to 256 x 256 % Convert it to grey image and display it [filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'},'Chose Image File'); myimg=imread(cat(2,pathname,filename)); if (size(myimg,3)==3) myimg=rgb2gray(myimg); end myimg = imresize(myimg,[256 256]); myimg=double(myimg); subplot(2,2,1); imshow(myimg,[]),title('Original Image'); [M,N] = size(myimg); % Find size %Preprocessing of the image for x=1:M for y=1:N myimg1(x,y)=myimg(x,y)*((-1)^(x+y)); end end % Find FFT of the image myfftimage = fft2(myimg1); subplot(2,2,2); imshow(myfftimage,[]); title('FFT Image'); % Define cut off frequency low = 30; band1 = 20; band2 = 50; %Define Filter Mask mylowpassmask = ones(M,N); mybandpassmask = ones(M,N); % Generate values for ifilter pass mask for u = 1:M for v = 1:N tmp = ((u-(M/2))^2 +(v-(N/2))^2)^0.5; if tmp > low mylowpassmask(u,v) = 0; end if tmp > band2 || tmp < band1; mybandpassmask(u,v) = 0; E.C. Department, Government Engineering College, Rajkot

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end end end % Apply the filter H to the FFT of the Image resimage1 = myfftimage.*mylowpassmask; resimage3 = myfftimage.*mybandpassmask; % Apply the Inverse FFT to the filtered image % Display the low pass filtered image r1 = abs(ifft2(resimage1)); subplot(2,2,3); imshow(r1,[]),title('Low Pass filtered image'); % Display the band pass filtered image r3 = abs(ifft2(resimage3)); subplot(2,2,4); imshow(r3,[]),title('Band Pass filtered image'); figure; subplot(2,1,1);imshow(mylowpassmask); subplot(2,1,2);imshow(mybandpassmask);

:: WORKSHEET :: [1] Instead of following pre-processing step in above program use fftshift function to shift FFT in the center. See changes in the result and write conclusion. %Preprocessing of the image for x=1:M for y=1:N myimg1(x,y)=myimg(x,y)*((-1)^(x+y)); end end

Remove above step and use following commands. myfftimage = fft2(myimg); myfftimage=fftshift(myfftimage);

Conclusion:

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Lab Manual of Fundamentals of Image Processing

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_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [2] Write a routine for high pass filter mask

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [2] Write a routine for high pass filter mask

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________


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EXPERIMENT NO. 10 AIM: Write a program in C and MATLAB/SCILAB for edge detection detection using different edge detection mask. Introduction: Image segmentation is to subdivide an image into its component regions or objects. Segmentation should stop when the objects of interest in an application have been isolated. Basic purpose of segmentation is to partition an image into meaningful regions for particular application The segmentation is based on measurements taken from the image and might be grey level, colour, texture, depth or motion. There are basically basically two types types of image segmentation segmentation approaches: [1] Discontinuity based: Identification of isolated points, lines or edges [2] Similarity based: Group pixels which has similar characteristics by thresholding, region growing, region splitting and merging Edge detection is discontinuity based image segmentation approach. Edges play a very important role in many image processing applications. It provides outline of an object. In physical plane, edges are corresponding to changes in material properties, intensity variations, discontinuity in depth. Pixels on the edges are called edge points. Edge detection techniques basically try to find out grey level transitions. Edge detection can be done by first order derivative and second order derivative operators. First order line detection 3x3 mask are:


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Popular edge detection masks:





Sobel operator performs well for image with noise compared to Prewitt operator because Sobel operator performs averaging along with edge detection. Because Sobel operator gives smoothing effect, spurious edges will not be detected by it.

Second derivative operators are sensitive to the noise present in the image so it is not directly used to detect edge but it can be used to extract secondary information like …  Used to find whether point is on darker side or white side depending on sign of the result  Zero crossing can be used to identify exact location of edge whenever there is gradual transitions in the image

MATLAB Code using standard function:

% Experiment 10 % Program for edge detection using standard masks % Image Processing Lab, EC Department, GEC Rajkot clear all all;; [filename,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif' [filename,pathname]=uigetfile({ '*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif',,'IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'}, (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)' },'Choose 'Choose Image'); Image'); A=imread(filename); if (size(A,3)==3) (size(A,3)==3) A=rgb2gray(A); end imshow(A); figure; BW = edge(A,'prewitt' edge(A,'prewitt'); ); subplot(3,2,1); imshow(BW);title('Edge imshow(BW);title('Edge detection with prewitt mask'); mask' ); BW = edge(A,'canny' edge(A,'canny'); ); subplot(3,2,2); imshow(BW);;title('Edge imshow(BW);;title('Edge detection with canny mask'); mask' ); BW = edge(A,'sobel' edge(A,'sobel'); ); subplot(3,2,3); imshow(BW);;title('Edge imshow(BW);;title('Edge detection with sobel mask'); mask' ); BW = edge(A,'roberts' edge(A,'roberts'); ); subplot(3,2,4); imshow(BW);;title('Edge imshow(BW);;title('Edge detection with roberts mask'); mask' ); E.C. Department, Department, Government Engineering College, Rajkot

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BW = edge(A,'log' edge(A,'log'); ); subplot(3,2,5); imshow(BW);;title('Edge imshow(BW);;title('Edge detection with log '); '); BW = edge(A,'zerocross' edge(A,'zerocross'); ); subplot(3,2,6); imshow(BW);;title('Edge imshow(BW);;title('Edge detection with zerocorss'); zerocorss'); MATLAB Code for edge detection using convolution in spatial domain

% EC Department, GEC Rajkot % Experiment 9 % Program to demonstrate various point and edge detection mask % Image Processing Lab, EC Department, GEC Rajkot clear all all;; clc; while 1 while 1 K = menu('Choose menu('Choose mask', mask','Select Image File', File','Point Detect', Detect','Horizontal line detect', detect','Vertical line detect', detect','+45 Detect', Detect','-45 Detect', Detect','ractangle Detect', Detect','exit' 'exit')) M=[-1 0 -1; 0 4 0; -1 0 -1;] % Default mask switch K switch K case 1, case 1, [namefile,pathname]=uigetfile({'*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif' [namefile,pathname]=uigetfile({ '*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif',,'IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)'}, (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)' },'Chose 'Chose GrayScale Image'); Image'); data=imread(strcat(pathname,namefile)); %data=rgb2gray(data); imshow(data); case 2, case 2, M=[-1 -1 -1;-1 8 -1;-1 -1 -1]; % Mask for point detection case 3, case 3, M=[-1 -1 -1; 2 2 2; 2 ; -1 -1 -1]; % Mask for horizontal edges case 4, case 4, M=[-1 2 -1; -1 2 -1; -1 2 -1]; % Mask for vertical edges case 5, case 5, M=[-1 -1 2; -1 2 -1; 2 -1 -1]; % Mask for 45 degree diagonal line case 6, case 6, M=[2 -1 -1;-1 2 -1; -1 -1 2]; % Mask for -45 degree de gree diagonal line case 7, case 7, M=[-1 -1 -1;-1 8 -1;-1 -1 -1]; case 8, case 8, break;; break otherwise,, otherwise msgbox('Select msgbox('Select proper mask'); mask'); end outimage=conv2(double(data),double(M)); figure; imshow(outimage); end close all %Write an image to a file Lab Manual of Fundamentals Fundamentals of Image Processing

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imwrite(mat2gray(outimage),'outimage.jpg','quality',99);

:: WORKSHEET :: [1] Get mask for “Prewitt”, “Canny”, “Sobel” from the literature and write MATLAB/SCILAB program for edge detection using 2D convolution

_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ E.C. Department, Government Engineering College, Rajkot

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_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ [2] Understand following C++ program written for edge detection. Different member functions are written for image class. Image class and member function are written in Image.h header file and used in main program in file image.cpp / / f i l e i mage. h # i ncl ude # i ncl ude # i ncl ude # i ncl ude # def i ne r ows 10 # def i ne col s 10 cl ass i mage { pr i vat e: i nt dat a[ 10] [ 10] ; publ i c: voi d wr i t e_i mage( char [ ] ) ; voi d read_i mage( char [ ] ) ; voi d edge_det ect ( char [ ] ) ; }; voi d i mage: : wr i t e_i mage( char f i l ename[ ] ) { FI LE * f 1; i nt i , j ; i nt k=r ows*col s; f 1=f open( f i l ename, " wb" ) ; f or ( i =0; i
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c i n>>dat a[ i ] [ j ] ; } } f wr i t e( dat a, s i zeof ( i nt ) , k, f 1) ; f cl os e( f 1) ; } voi d i mage: : r ead_i mage( char f i l ename[ ] ) { FI LE * f 1; i nt i , j ; f 1=f open( f i l ename, "r b") ; i nt k=r ows*col s; f r ead( dat a, s i z eof ( i nt ) , k, f 1) ; f or ( i =0; i 16) sum=15; out i mage[ i ] [ j ] =sum; } } cout <<" \ nEdge det ect i on usi ng Qui ck mask: " <
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}

//file Image.cpp # i ncl ude " d: \ i mage\ i mage. h" voi d hel p( ) ; voi d mai n( i nt ar gc, char * argv[ ] ) { i mage I ; char f i l e_name[ 30] ; cl r scr ( ) ; i f ( ar gv[ 1] ) { i f ( ! ar gv[ 2] ) { pr i nt f ( " Ent er f i l e name: " ) ; scanf ( "%s", f i l e_name) ; } el se { st r cpy( f i l e_name, ar gv[ 2] ) ; } i f ( ! s t r c mp( ar gv[ 1] , " wr i t e" ) ) {I . wr i t e_i mage( f i l e_name) ; } el se i f ( ! st r cmp( ar gv[ 1] , "r ead") ) {I . r ead_i mage( f i l e_name) ; } el se i f ( ! st r cmp( ar gv[ 1] , "edge") ) { I . edge_det ect ( f i l e_name) ; } el s e i f ( ! s t r c mp( ar gv[ 1] , " hi s t " ) ) { } el se {hel p( ) ; } } el se {hel p( ) ; } } voi d hel p( ) { pr i nt f ( " \ n\ t \ t I mage Pr o ces s i ng Tut or i al ! " ) ; pr i nt f ( " \ t \ t \ t Us age : \ n" ) ; pr i nt f ( "\ t \ t \ t i mage \ n" ) ; pr i nt f ( " \ t \ t \ t For exampl e : \ n " ) ; pr i nt f ( " \ t \ t \ t i mage wr i t e dat a : To wr i t e i mage i n \ " dat a\ " f i l e\ n" ) ; pr i nt f ( " \ n\ t \ t \ t Ot her Oper at i ons : \ n" ) ; pr i nt f ( " \ n\ t \ t r ead: r ead i mage\ n" ) ; pr i nt f ( " \ n\ t \ t edge : Edge det ec t i on\ n" ) ; pr i nt f ( " \ n\ t \ t hi s t : di s pl ay hi s t ogr am of i mage\ n" ) ; }


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Output:

Original Image of size 10 10:

Edge detection after applying quick mask:

1111111111 00 00 00 00 00 00 00 00 00 00 1111111111 00 00 00 00 00 00 00 00 00 00

1111111111

00 00 00 00 00 00 00 00 00 00

1119999111 1119999111

00 00 00 15 16 16 15 00 00 00

1119999111

00 00 00 16 00 00 16 00 00 00

1119999111

00 00 00 16 00 00 16 00 00 00

1111111111 1111111111 1111111111 Exercise:

[1] Modify above program for edge detection using kirsch, prewitt & sobel mask [2] Add member function LPF() to image class for spatial filtering of given image Hint: Use following low pass convolution mask for low pass filtering

1/6 *

0

1

0

1

2 1

0

1

0


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EXPERIMENT NO. 11 AIM: Write and execute program for image morphological operations: erosion and dilation Introduction:  Morphology is a branch of biology that deals with form and structure of animal and plant  In image processing, we use mathematical morphology to extract image components which are useful in representation and description of region shape such as … Boundaries, Skeletons, Convex hull, Thinning, Pruning etc.  Two Fundamental morphological operations are: Erosion and dilation  Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. Erosion and dilation are two fundamental image morphological operations.  

Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. Dilation operation: The value of the output pixel is the maximum value of all the pixels in the input pixel’s neighborhood. In a binary image, if any of the pixels is set to the value 1, the output pixel is set to 1.



Erosion operation: The value of the output pixel is the minimum value of all the pixels in the input pixel’s neighborhood. In a binary image, if any of the pixels is set to 0, the output pixel is set to 0.



Opening and closing operations can be done by combination of erosion and dilation in different sequence.

Program:

% Pr ogr am t o demonst r at e Er osi on and Di l at i on % I mage Pr ocessi ng Lab, EC Depar t ment , GEC Raj kot cl ear al l ; cl c; whi l e 1 K = menu( ' Er osi on and di l at i on demo' , ' Choose I mage' , ' Choose 3x3 Mask' , ' Choose 5x5 Mask' , ' Choose St r uct ur e I mage' , ' Er os i on' , ' Di l at i on' , ' Openi ng' , ' Cl os i ng' , ' EXI T' ) %B=[ 1 1 1; 1 1 1; 1 1 1; ] ; Lab Manual of Fundamentals of Image Processing

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B = st r el ( ' di s k' , 9 ) ; %B = s t r el ( ' di s k' , 5) ; swi t ch K case 1, [ namef i l e, pat hname] =ui get f i l e( {' * . bmp; * . t i f ; * . t i f f ; *. j pg; *. j peg; * . gi f ' , ' I MAGE Fi l es ( * . bmp, * . t i f , * . t i f f , * . j pg, *. j peg, * . gi f ) ' }, ' Chose Gr ayScal e I mage' ) ; A=i mr ead( st r cat ( pat hname, namef i l e) ) ; %dat a=r gb2gr ay( dat a) ; i mshow( A) ; case 2, B=[ 1 1 1; 1 1 1; 1 1 1; ] ; case 3, B=[ 1 1 1 1 1; 1 1 1 1 1; 1 1 1 1 1; 1 1 1 1 1; 1 1 1 1 1; ] ; case 4, [ namef i l e, pat hname] =ui get f i l e( {' * . bmp; * . t i f ; * . t i f f ; *. j pg; *. j peg; * . gi f ' , ' I MAGE Fi l es ( * . bmp, * . t i f , * . t i f f , * . j pg, *. j peg, * . gi f ) ' }, ' Chose Gr ayScal e I mage' ) ; B=i mr ead( st r cat ( pat hname, namef i l e) ) ; %dat a=r gb2gr ay( dat a) ; f i gur e; i mshow( B) ; case 5, C=i mer ode( A, B) ; f i gur e; i mshow( C) ; case 6, C=i mdi l at e( A, B) ; f i gur e; i mshow( C) case 7, C=i mdi l at e( A, B) ; D=i mer ode( C, B) ; f i gur e; i mshow( D) case 8, C=i mer ode( A, B) ; D=i mdi l at e( C, B) ; f i gur e; i mshow( D) case 9, br eak; ot her wi se, E.C. Department, Government Engineering College, Rajkot

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msgbox( ' Sel ect pr oper mask' ) ; end end cl ose al l %Wr i t e an i mage t o a f i l e i mwr i t e( mat 2gr ay( out i mage) , ' out i mage. j pg' , ' qual i t y' , 99) ;

WORKSHEET :: [1] What is cryptography? ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------[2] What is sateganography? ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------[3] How watermarking differs from cryptography and sateganography? ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Execute above program given in this experiment on suitable image, cut and paste resultant images on separate page and write your conclusion.


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EXPERIMENT NO. 12 AIM: To write and execute program for wavelet transform on given image and perform inverse wavelet transform to reconstruct image. Introduction: Wavelet transform is relatively recent signal and image processing tool which has many applications. Basis functions of Fourier transform is sinusoids while basis functions of wavelet transform is wavelets.    

Wavelets are oscillatory functions vanishing outside the small interval hence, they are called wavelets. wave-let (small fraction of wave). Wavelets are building blocks of the signal. Wavelets are the functions which are well suited to the expansion of real time non-stationary signals. Wavelets can be used to de-correlate the correlations present in real time signal such as speech/audio, video, biomedical, seismic etc.

General block diagram of two dimensional wavelet transform:


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Following standard matlab functions are used in this experiment. [1] wavedec2 : Function for multi-level decomposition of 2D data using wavelet transform. [C,S] = WAVEDEC2(X,N,'wname') returns the wavelet decomposition of the matrix X at level N, using the wavelet named in string 'wname' (wavelet name can be Daubechies wavelet db1, db2, db3 or any other wavelet). Outputs are the decomposition vector C and the corresponding bookkeeping matrix S. N is level of decomposition which must be positive integer. [2] appcoef2: This function utilize tree developed by wavelet decomposition function and constructs approximate coefficients for 2D data. A = APPCOEF2(C,S,'wname',N) computes the approximation coefficients at level N using the wavelet decomposition structure [C,S] which is generated by function wavedec2. [3] detcoef2: This function utilize tree develet by wavelet decomposition function and generates detail coefficients for 2D data. D = DETCOEF2(O,C,S,N) extracts from the wavelet decomposition structure [C,S], the horizontal, vertical or diagonal detail coefficients for O = 'h' or 'v' or 'd', for horizontal, vertical and diagonal detail coefficients respectively. [4] waverec2: Multilevel wavelet 2D reconstruction (Inverse wavelet transform) WAVEREC2 performs a multilevel 2-D wavelet reconstruction using wavelet named in string 'wname' (wavelet name can be Daubechies wavelet db1, db2, db3 or any other wavelet). X = WAVEREC2(C,S,'wname') reconstructs the matrix X based on the multilevel wavelet decomposition structure [C,S] which is generated by wavedec2


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Program: clc; close; [namefile,pathname]=uigetfile({ '*.bmp;*.tif;*.tiff;*.jpg;*.jpeg;*.gif','IMAGE Files (*.bmp,*.tif,*.tiff,*.jpg,*.jpeg,*.gif)' },'Chose GrayScale Image'); X=imread(strcat(pathname,namefile)); if (size(X,3)==3) X=rgb2gray(X); end imshow(X); % Perform wavelet decomposition at level 2. [c,s] = wavedec2(X,2,'db1'); figure; imshow(c,[]); title('Wavelet decomposition data generated by wavedec2'); figure; %Calculate first level approx. and detail components ca1 = appcoef2(c,s,'db1',1); subplot(2,2,1);imshow(ca1,[]);title('First level approx'); ch1 = detcoef2('h',c,s,1); subplot(2,2,2);imshow(ch1,[]);title('First level horixontal detail'); cv1 = detcoef2('v',c,s,1); subplot(2,2,3);imshow(cv1,[]);title('First level vertical detail'); cd1 = detcoef2('d',c,s,1); subplot(2,2,4);imshow(cd1,[]);title('First level diagonal detail'); %Calculate second level approx. and detail components figure; ca2 = appcoef2(c,s,'db1',2); subplot(2,2,1);imshow(ca2,[]);title('Second level approx'); ch2 = detcoef2('h',c,s,2); subplot(2,2,2);imshow(ch2,[]);title('Second level horizontal detail'); cv2 = detcoef2('v',c,s,2); subplot(2,2,3);imshow(cv2,[]);title('Second level vertical detail'); cd2 = detcoef2('d',c,s,2); subplot(2,2,4);imshow(cd2,[]);title('Second level diagonal detail'); figure; a0 = waverec2(c,s,'db1'); imshow(a0,[]);title('Reconstructed image using inverse wavelet transform'); Execute program given in this experiment on suitable image, cut and paste resultant images on separate page and write your conclusion.


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Fundamentals of Image Processing Lab Manual 2014.pdf

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