% Implementation of SRAD Filter % Ref :Yongjian Yu and Scott T. Acton, "Speckle Reducing Anisotropic % Diffusion",IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 11, NO. 11, NOVEMBER 2002 % Jeny Rajan, Chandrashekar P.S % usage S=srad(I,T) % I- noisy image % T - threshold (greater than 0). function S=srad(I,T) tic [x y]=size(I); I=double(I); Ic=double(I); delta_t = 0.08; t=1; eps=0.00000000001; for t=1:T qt=exp(-t*.2); [Ix,Iy] = gradient(Ic); di=sqrt(Ix.^2+Iy.^2); di2=del2(Ic); T1=0.5*((di./(Ic+eps)).^2); T2=0.0625*((di2./(Ic+eps)).^2); T3=(1+(0.25*(di2./(Ic+eps)))).^2; T=sqrt((T1-T2)./(T3+eps)); dd=(T.^2-qt.^2)./((qt.^2*(1+qt.^2)+eps)); cq=1./(1+dd); [D1,D2]=gradient(cq.*Ix); [D3,D4]=gradient(cq.*Iy); D=D1+D4; Ic=real(Ic+delta_t .*D); end toc S=uint8(Ic); %Frost filter for speckle noise reduction %Author : Jeny Rajan function [ft]=frost(I) % I is the noisy input image tic [x y z]=size(I); I=double(I); K=1; N=I; for i=1:x for j=1:y if (i>1 & i1 & j
c=mat-mn; c2=c.^2; c3=c/(c2+.0000001); Cs=0.25*sum(sum(c3)); m(1)=exp(-K*Cs*d(1)); m(2)=exp(-K*Cs*d(2)); m(3)=exp(-K*Cs*d(3)); m(4)=exp(-K*Cs*d(4)); ms=sum(sum(m)); mp=m/ms; N(i,j)=sum(sum(mp.*mat)); end end end toc ft=uint8(N);
%Kuan filter for speckle noise reduction %Usage - kuan(I) %I is the noisy image (gray level image m x n x 1) function [kn]=kuan(I) [x y z]=size(I) I=double(I); for i=1:x i for j=1:y % Checking first and last pixel of first row% if (i==1 & j==1) mat(1)=0; mat(2)=0; mat(3)=0; mat(4)=0; mat(5)=I(i,j); mat(6)=I(i,j+1); mat(7)=0; mat(8)=I(i+1,j); mat(9)=I(i+1,j+1); end if (i==1 & j==y) mat(1)=0; mat(2)=0; mat(3)=0; mat(4)=I(i,j-1); mat(5)=I(i,j); mat(6)=0; mat(7)=I(i+1,j-1); mat(8)=I(i+1,j); mat(9)=0; end % Checking first and last pixel of last row%
if (i==x & j==1) mat(1)=0; mat(2)=I(i-1,j); mat(3)=I(i-1,j+1); mat(4)=0; mat(5)=I(i,j); mat(6)=I(i,j+1); mat(7)=0; mat(8)=0; mat(9)=0; end if (i==x & j==y) mat(1)=I(i-1,j-1); mat(2)=I(i-1,j); mat(3)=0; mat(4)=I(i,j-1); mat(5)=I(i,j); mat(6)=0; mat(7)=0; mat(8)=0; mat(9)=0; end % Checking rest of the image% if (i>1 & i1 & j
N=zeros(x,y,z); for i=1:x i for j=1:y % Checking first and last pixel of first row% if (i==1 & j==1) mat(1)=0; mat(2)=0; mat(3)=0; mat(4)=0; mat(5)=I(i,j); mat(6)=I(i,j+1); mat(7)=0; mat(8)=I(i+1,j); mat(9)=I(i+1,j+1); end if (i==1 & j==y) mat(1)=0; mat(2)=0; mat(3)=0; mat(4)=I(i,j-1); mat(5)=I(i,j); mat(6)=0; mat(7)=I(i+1,j-1); mat(8)=I(i+1,j); mat(9)=0; end % Checking first and last pixel of last row% if (i==x & j==1) mat(1)=0; mat(2)=I(i-1,j); mat(3)=I(i-1,j+1); mat(4)=0; mat(5)=I(i,j); mat(6)=I(i,j+1); mat(7)=0; mat(8)=0; mat(9)=0; end if (i==x & j==y) mat(1)=I(i-1,j-1); mat(2)=I(i-1,j); mat(3)=0; mat(4)=I(i,j-1); mat(5)=I(i,j); mat(6)=0; mat(7)=0; mat(8)=0; mat(9)=0; end % Checking rest of the image% if (i>1 & i1 & j
mat(3)=I(i-1,j+1); mat(4)=I(i,j-1); mat(5)=I(i,j); mat(6)=I(i,j+1); mat(7)=I(i+1,j-1); mat(8)=I(i+1,j); mat(9)=I(i+1,j+1); end y1=I(i,j); ybar=mean(mean(mat)); if ybar~=0 ystad=std2(mat); ENL=(ybar/ystad)^2; sx2=((ENL*(ystad)^2)-(ybar)^2)/(ENL+1); xcap=ybar+(sx2*(y1-ybar)/(sx2+(ybar^2/ENL))); N(i,j)=xcap; else N(i,j)=y1; end end end le=uint8(N);
f=zeros(250,250); %1 f(20:229,20:27)=1; %2 f(20:229,44:51)=1; %3 f(20:229,68:75)=1; %4 f(20:229,92:99)=1; %5 f(20:229,116:123)=1; %6 f(20:229,140:147)=1; %7 f(20:229,164:171)=1; %8 f(20:229,188:195)=1; %9 f(20:229,212:219)=1; imshow(f,'InitialMagnification','fit');
%ËãÊõ¾ùÖµÂ˲¨ %A_1=spfilt(f,'amean',3,3); %figure,imshow(A_1); %A_2=spfilt(f,'amean',5,5); %figure,imshow(A_2);
%3¡Á3ËãÊõ¾ùÖµÂ˲¨ %5¡Á5ËãÊõ¾ùÖµÂ˲¨
%A_3=spfilt(f,'amean',7,7); %figure,imshow(A_3);
%7¡Á7ËãÊõ¾ùÖµÂ˲¨
%¼¸ºÎ¾ùÖµÂ˲¨ %B_1=spfilt(f,'gmean',3,3); %figure,imshow(B_1); %B_2=spfilt(f,'gmean',5,5); %figure,imshow(B_2); %B_3=spfilt(f,'gmean',7,7); %figure,imshow(B_3);
%3¡Á3¼¸ºÎ¾ùÖµÂ˲¨ %5¡Á5¼¸ºÎ¾ùÖµÂ˲¨ %7¡Á7¼¸ºÎ¾ùÖµÂ˲¨
%г²¨¾ùÖµ %C_1=spfilt(f,'hmean',3,3); %figure,imshow(C_1); %C_2=spfilt(f,'hmean',5,5); %figure,imshow(C_2); %C_3=spfilt(f,'hmean',7,7); %figure,imshow(C_3);
%3¡Á3г²¨¾ùÖµÂ˲¨ %5¡Á5г²¨¾ùÖµÂ˲¨ %7¡Á7г²¨¾ùÖµÂ˲¨
%Äæг²¨¾ùÖµ %D_1=spfilt(f,'chmean',3,3); %figure,imshow(D_1); %D_2=spfilt(f,'chmean',5,5); %figure,imshow(D_2); %D_3=spfilt(f,'chmean',7,7); %figure,imshow(D_3);
%ÖÐÖµÂ˲¨ %E_1=spfilt(f,'median',3,3); %figure,imshow(E_1); %E_2=spfilt(f,'median',5,5); %figure,imshow(E_2); %E_3=spfilt(f,'median',7,7); %figure,imshow(E_3); %ÖеãÂ˲¨ %F_1=spfilt(f,'midpoint',3,3); %figure,imshow(F_1); %F_2=spfilt(f,'midpoint',5,5); %figure,imshow(F_2); %F_3=spfilt(f,'midpoint',7,7); %figure,imshow(F_3); %×î´óÖµÂ˲¨ %G_1=spfilt(f,'max',3,3); %figure,imshow(G_1); %G_2=spfilt(f,'max',5,5); %figure,imshow(G_2); %G_3=spfilt(f,'max',7,7); %figure,imshow(G_3); %×îСֵÂ˲¨ %H_1=spfilt(f,'min',3,3);
%3¡Á3Äæг²¨¾ùÖµÂ˲¨ %5¡Á5Äæг²¨¾ùÖµÂ˲¨ %7¡Á7Äæг²¨¾ùÖµÂ˲¨
%3¡Á3ÖÐÖµÂ˲¨ %5¡Á5ÖÐÖµÂ˲¨ %7¡Á7ÖÐÖµÂ˲¨
%3¡Á3ÖеãÂ˲¨ %5¡Á5ÖеãÂ˲¨ %7¡Á7ÖеãÂ˲¨
%3¡Á3×î´óÖµÂ˲¨ %5¡Á5×î´óÖµÂ˲¨ %7¡Á7×î´óÖµÂ˲¨
%3¡Á3×î´óÖµÂ˲¨
%figure,imshow(H_1); %H_2=spfilt(f,'min',5,5); %figure,imshow(H_2); %H_3=spfilt(f,'min',7,7); %figure,imshow(H_3);
%5¡Á5×î´óÖµÂ˲¨ %7¡Á7×î´óÖµÂ˲¨
% alpha-trimmed ƽ¾ù %H_1=spfilt(f,'atrimmed',3,3); %figure,imshow(H_1); %H_2=spfilt(f,'atrimmed',5,5); %figure,imshow(H_2); %H_3=spfilt(f,'atrimmed',7,7); %figure,imshow(H_3);
%3¡Á3alpha-trimmed ƽ¾ù %5¡Á5alpha-trimmed ƽ¾ù %7¡Á7alpha-trimmed ƽ¾ù
%linear filtering using geometric filter--GAUSSIAN NOISE a=imread('lc.bmp'); a=rgb2gray(a); a=im2double(a); imshow(a); [row col]=size(a); na=imnoise(a,'gaussian'); figure,imshow(na); fa=na; for i=1:row-2, for j=1:col-2, ad=1; for g=i:i+2, for h=j:j+2, ad=ad*na(g,h); end; end; %ad=power(ad,1/9); ad=ad.^(1/9); fa(i+1,j+1)=ad; end; end; figure,imshow(fa);
