IMAGE FUSION USING DWT
Main Project Presentation
Introduction
Image fusion – a technique that integrates complementary information from multiple image sensor data such that the new image are more suitable for processing tasks. An image pyramid can be described as collection of low- or band-pass copies of an original image in which both the band limit and sample density are reduced in regular steps. The basic strategy of image fusion based on pyramids is to use a feature selection rule to construct a fused pyramid representation from the pyramid representations of the original data. The composite image is obtained by taking an inverse pyramid transform.
DECISION RULE BASED IMAGE FUSION USING WAVELET TRANSFORM
In recent years, many solutions to image fusion have been proposed. This paper presents an effective multi-resolution image fusion methodology, which which is wavelet based image fusion. fusion. Fusion process is applied in the clinical case: the study of some particular disease by MR/SPECT fusion. The effectiveness of the proposed model is demonstrated via results comparison with several other image fusion methods.
Literature survey
The technique that was used before was called multi resolution analysis Existing System
Existing System
Fusion framework in feature-level. Effective multi-sensor image data fusion methodology on the basis of discrete wavelet transform theory Self-Organizing Neural Network.
Proposed System Fusion
framework in Decision level
Using
discrete wavelet transform method
Fuzzy
logic Neural Networks
Wavelet Transform
What is wavelet Transform: Wavelet Transform is a type of signal representation that can give the frequency content of the signal at a particular instant of time.
Wavelet Transform
Why need wavelet transform?
Wavelet analysis has advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.
Discrete Wavelet Transform(DWT)
Wavelet transform is related to two functions:the scaling function Ø(x) and mother wavelet ψ(x).
hi, gi are called coefficients(filters).
hi : Smooth filters.(Lowpass filters)
gi : Detail filters.(Highpass filters)
Each kind of wavelet transform defines its own filter coefficients. Daubechies.
1D Discrete Wavelet Transform
2D Discrete Wavelet Transform
Fuzzy Logic Definition “Fuzzy logic a superset of Boolean logic dealing with the concept of
partial truth – truth values between ‘completely true’ and ‘completely false’.It was introduced mainly to model the uncertainty of natural language.” Fuzzy set to describe the degree to which two numbers are similar, for example, degree of similarity of temperatures
1.00 0.75 0.50 0.25 0.00
very quite slightly
-20
-10
0
10
difference (°C)
20
New Approach
Discrete wavelet transform can offer a more precise way for image analysis.It decomposes a image into low frequency band and high frequency band in different levels, and it can also be reconstructed at these levels.
When images are merged in this method different frequencies are processed differently.
Improves the quality of the new image since it works on Feature extraction.
The fusion algorithm is performed at the pixel level.
DWT Sub-band Structure Horizontal(Rows) L
NxM
L
2
LL
H
2
LH
L
2
HL
H
2
HH
2
Image with resolution Level R
H
Vertical(Columns)
2 N/2 x M
L: Lowpass filter H: Highpass filter 2: downsample by
N/2 x M/2
Image corresponding to resolution Level R-1 Detail Image correspondi ng to information visible at the resolution Level R
DWT Sub-band Structure LL: Horizontal Low pass & Vertical Low pass
LH: Horizontal Low pass & Vertical High pass
HL: Horizontal High pass & Vertical Low pass
HH: Horizontal High pass & Vertical High pass
DWT Sub-band Structure
Stage 1
Stage 2
Stage 3 DWT with D=3 stages
A DWT Example
LL1
LH1
LL2
2
HL
LL0
LH
LH2
HH2
2
HL1
HH1
HL2
HH2
Functional Flow Diagram Input Image A
Filtering in the domain of spatial frequency Wavelet decomposition
Input Image B Image reconstruction
Inverse wavelet decomposition
Fused Image
Fusion Rules
Fusion Decision Map
Functional Flow Diagram 2
Tools
MATLAB 6.5
Implementation
Relevant wavelet theory
Since image is 2-D signal, we will mainly focus on the 2-D 2 -D wavelet transforms. After one level of decomposition, there will be four frequency bands, namely Low-Low (LL), Low-High (LH), High-Low (HL) and HighHigh (HH).
Implementation
The next level decomposition is just apply to the LL band of the current decomposition stage, which forms a recursive decomposition procedure.
The frequency bands in higher decomposition levels will have smaller size .
Implementation The
wavelet transforms w of the two registered input images are computed and these transforms tr ansforms are combined utilizing some kind of fusion rule
The
fusion rule is actually a set of fusion rules ,where j = 1, .., J and c = 1,. . . ,7, which define the fusion of each pair of corresponding channels for each band
GUI - EXISTING TECHNIQUES
GUI – WAVELET APPROACH
GUI – FUZZY BASED
GUI – WAVELET AND FUZZY BASED
Advantages
No need to divide the input coding into non-overlapping 2-D blocks, it has higher compression ratios avoid blocking artifacts. Allows good localization both in time and spatial frequency domain. Transformation of the whole image introduces inherent scaling Better identification of which data is relevant to human perception higher compression ratio (64:1 vs. 500:1)
Applications
NAVIGATION AID
MEDICAL IMAGING
REMOTE SENSING
MERGING OUT-OF-FOCUS IMAGES
Applications
Intelligent robots •Require motion control, based on feedback from the environment from visual, tactile, force/torque, and other types of sensors •Stereo camera fusion •Intelligent viewing control •Automatic target recognition and tracking
Applications
Medical image •Fusing X-ray computed topography (CT) and magnetic resonance (MR) images • Computer assisted surgery • Spatial registration of 3-D surface
Applications
Manufacturing •
Electronic circuit and component inspection
•
Product surface measurement and inspection non-destructive material inspection
•
Manufacture process monitoring
•
Complex machine/device diagnostics
•
Intelligent robots on assembly lines
Applications
Military and law enforcement
•
Detection, tracking, identification of ocean (air,ground)target/event
•
Concealed weapon detection
•
Battle-field monitoring
•
Night pilot guidance
References BASE PAPER : DAVID L. HALL and JAMES LLINAS, An Introduction to Multisensor Data Fusion, Proceedings of IEEE, 85, 1 (Jan. 1997) Barbara Zitova, Jan Flusser, Image registration methods: a survey. Image and Vision Computing 21
References RELATED PAPERS : L. J. Chipman and T. M. Orr, “Wavelets and image fusion,” in Proceedings of the IEEE International International Conference on Image Processing, Washington D.C., October 1995, pp. 248– 251 (2003) L.J. Chipman, T.M. Orr, and L.N. Lewis. Wavelets and image fusion.IEEE Transactions on Image Processing, 3:248–251, 1995. linage fusion techniqcs Sinione.Giovanni and Farina. Alfonso and Morahito. Francesco and Scmico. Sebastiano Bruno and Bruzzone.Lorcnzo (U). Technical Report DIT-02-025, University of Trento.
