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.
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');
