tp_5.rar_TP

clear all; img=imread('eight.tif'); %bruiter l'image A = imnoise(img,'salt & pepper', 0.02); % A=imnoise(img,'poisson'); % A= imnoise(img,'gaussian',0.1,0.1); %A = imnoise(img,'speckle',0.04); Adbl=im2double(A); B=Miroir_Expand(Adbl); B=Miroir_Expand(B); [Em,En]=size(B); for k=4:Em-2 for l=4:En-2 V=[B(k-2,l-2) B(k-2,l-1) B(k-2,l) B(k-2,l+1) B(k-2,l+2); ... B(k-1,l-2) B(k-1,l-1) B(k-1,l) B(k-1,l+1) B(k-1,l+2);... B(k,l-2) B(k,l-1) B(k,l) B(k,l+1) B(k,l+2); ... B(k+1,l-2) B(k+1,l-1) B(k+1,l) B(k+1,l+1) B(k+1,l+2);... B(k+2,l-2) B(k+2,l-1) B(k+2,l) B(k+2,l+1) B(k+2,l+2)]; % les neuf voisinages et on les arranges dans une matrice U 9x9 U=zeros(9,9); VB1=V(1:3,1:3); U(:,1)=[VB1(1,:)';VB1(2,:)';VB1(3,:)']; VB2=V(2:4,1:3); U(:,2)=[VB2(1,:)';VB2(2,:)';VB2(3,:)']; VB3=V(3:5,1:3); U(:,3)=[VB3(1,:)';VB3(2,:)';VB3(3,:)']; VB4=V(1:3,2:4); U(:,4)=[VB4(1,:)';VB4(2,:)';VB4(3,:)']; VB5=V(2:4,2:4); U(:,5)=[VB5(1,:)';VB5(2,:)';VB5(3,:)']; VB6=V(3:5,2:4); U(:,6)=[VB6(1,:)';VB6(2,:)';VB6(3,:)']; VB7=V(1:3,3:5); U(:,7)=[VB7(1,:)';VB7(2,:)';VB7(3,:)']; VB8=V(2:4,3:5); U(:,8)=[VB8(1,:)';VB8(2,:)';VB8(3,:)']; VB9=V(3:5,3:5); U(:,9)=[VB9(1,:)';VB9(2,:)';VB9(3,:)']; %calcul des variances v=std(U); % affectation [minv,index]=min(v); B(k,l) = mean( U(:,index) ); % w=tris_croissant(U(:,index)); %B(i,j)= w(5); end end Adbl=Miroir_Extract(B); Adbl=Miroir_Extract(Adbl); Aint=im2uint8(Adbl); figure;imshow(Aint,[]); title('image filtr�e en nagmod ')