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diff --git a/125/CH6/EX6.13/Fig6_13.sce b/125/CH6/EX6.13/Fig6_13.sce
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+//Caption: Scilab code to perform wiener filtering of the corrupted image

+//Fig6.13

+//Page 339

+close;

+clc;

+x = imread('E:\DIP_JAYARAMAN\Chapter6\flower2.jpg'); //SIVP toolbox

+x=double(rgb2gray(x));

+sigma = 50;

+Gamma = 1;

+alpha = 1; // It indicates Wiener filter

+[M N]=size(x);

+h = zeros(M,N);

+for i = 1:5

+    for j = 1:5

+        h(i,j) = 1/25;

+    end

+end

+Freqa = fft2d(x);

+Freqh = fft2d(h);

+y = real(ifft2d(Freqh.*Freqa)) //image degradation 

+y = y+25*rand(M,N,"normal"); //Adding random noise with normal distribution

+Freqy = fft2d(y);

+Powy = abs(Freqy).^2/(M*N);

+sFreqh = Freqh.*(abs(Freqh)>0)+1/Gamma*(abs(Freqh)==0);

+iFreqh = 1/sFreqh;

+iFreqh = iFreqh'.*(abs(Freqh)*Gamma>1)+Gamma*abs(sFreqh)*iFreqh*(abs(sFreqh)*Gamma<=1);

+iFreqh = iFreqh/(max(max(abs(iFreqh))));

+Powy = Powy.*(Powy>sigma^2)+sigma^2*(Powy<=sigma^2);

+Freqg = iFreqh.*(Powy-sigma^2)./(Powy-(1-alpha)*sigma^2);

+ResFreqa = Freqg.*Freqy;

+Resa = real(ifft2d(ResFreqa));

+x = uint8(x);

+y = uint8(y);

+Resa = uint8(Resa);

+ShowImage(x,'Original Image')

+title('Original Image')

+figure

+ShowImage(y,'Degraded Image')

+title('Degraded Image')

+figure

+ShowImage(Resa,'Restored Image')

+title('Restored Image')
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diff --git a/125/CH6/EX6.13/Fig6_13_Image_Restoration.JPG b/125/CH6/EX6.13/Fig6_13_Image_Restoration.JPG
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+++ b/125/CH6/EX6.13/Fig6_13_Image_Restoration.JPG