To replace the central pixel with the closest pixel based on Euclidean distance, you can find the pixel with the minimum distance and assign it to the center. Once you have the kernel, you can apply it to remove Gaussian noise using conv2. Refer the script below:
% Define kernel size
n = 3;
kernel = zeros(n, n);
center = ceil(n/2); % Central pixel index
% Compute Euclidean distance from center
for i = 1:n
for j = 1:n
kernel(i, j) = sqrt((i - center)^2 + (j - center)^2);
end
end
% Normalize kernel values
kernel = kernel / max(kernel(:));
% I am using a random image from internet
Original_Image = imread("https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png");
Noisy_Image = imnoise(Original_Image, 'gaussian', 0, 0.01);
% Apply kernel to each color channel
Noise_R = Noisy_Image(:,:,1);
Noise_G = Noisy_Image(:,:,2);
Noise_B = Noisy_Image(:,:,3);
New_R = conv2(Noise_R, kernel, 'same');
New_G = conv2(Noise_G, kernel, 'same');
New_B = conv2(Noise_B, kernel, 'same');
% Combine channels and display results
New_Image = cat(3, New_R, New_G, New_B);
montage({Original_Image, Noisy_Image, New_Image});
