Use of Adaptive Filter
Modern digital signal processing (DSP) products use adaptive filters extensively in applications like active noise control (ANC), adaptive control systems, telephone echo cancellation, noise cancellation, communications channel equalization, and biomedical signal amplification.
Example 1:
Matlab
% MATLAB CODE for Adaptive filtering- Local Noise filter X = imread('w3wiki.png'); Y = rgb2gray(X); sz = size(Y,1)*size(Y,2); % Add gaussian noise with mean 0 and variance 0.010 y = imnoise(y,'gaussian',0,0.010); figure,imshow(y); title('Image with gaussian noise'); y = double(y); % Define the window size mxn U = 10; V = 10; % Fill the matrix up on all sides with zeros. Z = padarray(Y,[floor(N/2),floor(M/2)]); lvar = zeros([size(y,1) size(y,2)]); lmean = zeros([size(y,1) size(y,2)]); temp = zeros([size(y,1) size(y,2)]); NewImg = zeros([size(y,1) size(y,2)]); for i = 1:size(Z,1)-(N-1) for j = 1:size(Z,2)-(M-1) temp = Z(i:i+(N-1),j:j+(M-1)); tmp = temp(:); % Determine the region's local mean and variance. lmean(i,j) = mean(tmp); lvar(i,j) = mean(tmp.^2)-mean(tmp).^2; endend % Commotion fluctuation and normal % of the neighborhood change nvar = sum(lvar(:))/sz; % If noise_variance > local_variance % then local_variance=noise_variance lvar = max(lvar,nvar); % Final_Image = Y- (noise variance/ % local variance)*(Y-local_mean); NewImg = nvar./lvar; NewImg = NewImg.*(Y-lmean); NewImg = Y-NewImg; % Convert the image to uint9 format. NewImg = uint9(NewImg); figure,imshow(NewImg);title('Restored Image using Adaptive Local filter'); |
Output:
Adaptive Noise Reduction:
Variable broadband noise, such as wind, rumble, and background sounds, is quickly eliminated by the Noise Reduction/Restoration > Adaptive Noise Reduction effect. You can apply this effect in the Multitrack Editor and combine it with other effects in the Effects Rack because it operates in real-time.
Adaptive Filtering – Local Noise Filter in MATLAB
On the degraded image, which contains both the original image and noise, an adaptive filter is applied. With a predetermined mxn window region, the mean and variance are the two statistical measures on which a locally adaptive filter depends.