i can't uderstand the image blurring in following code....what does the following blurring filter do? how does it work?

i read the following code for a filter that blurs and then deblurs the image but the resultant blurring image is not expected... can you please explain why is it so? even though the deblurred result actually looks like the blurred expectation....

iBlur = Blurred result

iRes = restored

function h = generateFilter(s,img)

h = [];

[width,height] = size(img);

for y = 1:height

for x = 1:width

h(x,y) = (s/(2*pi))^(-s*sqrt(x^2+y^2));

end

end

% 'h' is the bluring filter

h = h / sum(sum(h)); % normalize

end

function [iRes,h] = applyBlurFilter(s,img);

iRes = [];

h = generateFilter(s,img);

H = fft2(h);

IMG = fft2(img);

I_BLUR = IMG .* H;

figure; imshow(uint8(I_BLUR));

iRes = ifft2(I_BLUR);

figure; imshow(uint8(iRes));

end

a =imread(filename);

a = rgb2gray(a);

s = 0.2  % the value of s can be ranged from 0.1-0.5 to observe any sort of output....

applyBlurFilter(s,a)

please help...

[Walter Roberson]

You would have to tell us what you "expected".

It would probably also help if you described the difference between what you expected and what you got.

Answer 1

[Ryan]

I believe your unblurred is your expected blurred, and your blurred is actually the frequency domain for the blurred image. The filter is a Gaussian of some sort, I think it's an averaging filter due to the division by the sum(sum()).

Element wise division in the frequency domain with H will deblur it.

Answer 2

[Image Analyst]

All you do is inverse FFT your blurred image, so of course it looks blurred back in the spatial domain. If you didn't want that you'd have to do an "inverse filter" by dividing your fourier domain image by H before you IFFT it.