Adaptive filtering-local noise filter

Adaptive filter is performed on the degraded image that contains original image and noise. The mean and variance are the two statistical measures that a local adaptive filter depends with a defined mxn window region. 

MATLAB CODE:

A = imread('saturn.png');

B = rgb2gray(A);

sz = size(B,1)*size(B,2);

%Add gaussian noise with mean 0 and variance 0.005

B = imnoise(B,'gaussian',0,0.005);

figure,imshow(B); title('Image with gaussian noise');

B = double(B);

%Define the window size mxn

M = 5;

N = 5;

%Pad the matrix with zeros on all sides

C = padarray(B,[floor(M/2),floor(N/2)]);

lvar = zeros([size(B,1) size(B,2)]);

lmean = zeros([size(B,1) size(B,2)]);

temp = zeros([size(B,1) size(B,2)]);

NewImg = zeros([size(B,1) size(B,2)]);

for i = 1:size(C,1)-(M-1)

    for j = 1:size(C,2)-(N-1)

        

        

        temp = C(i:i+(M-1),j:j+(N-1));

        tmp =  temp(:);

             %Find the local mean and local variance for the local region        

        lmean(i,j) = mean(tmp);

        lvar(i,j) = mean(tmp.^2)-mean(tmp).^2;

        

    end

end

%Noise variance and average of the local variance

nvar = sum(lvar(:))/sz;

%If noise_variance > local_variance then local_variance=noise_variance

 lvar = max(lvar,nvar);     

 %Final_Image = B- (noise variance/local variance)*(B-local_mean);

 NewImg = nvar./lvar;

 NewImg = NewImg.*(B-lmean);

 NewImg = B-NewImg;

 %Convert the image to uint8 format.

 NewImg = uint8(NewImg);

figure,imshow(NewImg);title('Restored Image using Adaptive Local filter');