Adaptive filtering-local noise filter using Matlab

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,[round(M/2),round(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=reshape(temp,[1,(M*N)]);

             %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');