applying Ideal low-pass with Gaussian noise image

why we are not getting the right result of the filterd one?

I=imread('cameraman.tif');

figure(1);

original=imshow(I)

title('original image');

J = imnoise(I,'gaussian',40/255,(20/255)^2)

figure(2);

imshow(J)

title('Gussain noise');

% Saving the size of the input_image in pixels-

% M : no of rows (height of the image)

% N : no of columns (width of the image)

[M, N] = size(J);

% Getting Fourier Transform of the input_image

% using MATLAB library function fft2 (2D fast fourier transform)

FT_img = fft2(double(J));

% Assign Cut-off Frequency

r0 = 30;

% Designing filter

u = 0:(M-1);

idx = find(u>M/2);

u(idx) = u(idx)-M;

v = 0:(N-1);

idy = find(v>N/2);

v(idy) = v(idy)-N;

% MATLAB library function meshgrid(v, u) returns

% 2D grid which contains the coordinates of vectors

% v and u. Matrix V with each row is a copy

% of v, and matrix U with each column is a copy of u

[V, U] = meshgrid(v, u);

% Calculating Euclidean Distance

D = sqrt(U.^2+V.^2);

% Comparing with the cut-off frequency and

% determining the filtering mask

H = double(D <= r0);

% Convolution between the Fourier Transformed

% image and the mask

G = H.*FT_img;

% Getting the resultant image by Inverse Fourier Transform

% of the convoluted image using MATLAB library function

% ifft2 (2D inverse fast fourier transform)

output_image = real(ifft2(double(G)));

% Dispaly result

figure(3);

imshow(output_image);

title(' Result r0=30 ');

Answer 1

[Pratyush Roy]

Hi,

You can use the mat2gray function to map the output image values between 0 and 1, where 0 represents black, 1 represents white and the intermediate values represent various shades of gray. The code snippet below illustrates how to use the function:

% Dispaly result
figure(3);
imshow(mat2gray(output_image));
title(' Result r0=30 ');

Hope this helps!