Convolution on all dimensions of an image

Hello

I want this code to apply on RGB (and more dimentions) too.

what needs to be modified?

function output = mean_con(kernelSize) % get a kernel size from user (for a 3*3 they enter 3)
[path,~]=imgetfile(); % get an image
I=imread(path); 

kernelSize = floor(kernelSize/2); % radius

for j=(kernelSize+1):(size(I,1)-kernelSize)
    for i=(kernelSize+1):(size(I,2)-kernelSize)
        clip = I((j-kernelSize):(j+kernelSize),(i-kernelSize):(i+kernelSize));
        ave = imagemean(clip); % mean filter
        ave = mean2(clip);
        kernelMean=ave;
        IOut(j,i) = kernelMean;
    end
end

output=(uint8(IOut));
imshow(output);
end

Answer 1

[KALYAN ACHARJYA]

"I want this code to apply on RGB (and more dimentions) too."

One way: Try with different planes, and concatenation later, lets consider 3 D case

IOut=cat(3,ave{1},ave{2},ave{3})

If you are applying an average filter with a specific window (kernel) size, you can do this in a simpler way, have you looked at the vectorization approach to solve the same.

Answer 2

[Image Analyst]

You can use convn() but I don't see the point of blurring across color channels (blending the color channels together). I think you'd be better off blurring each color channel independently. That's what happens in the real world when an image is blurred.