Question

I blurred a grayscale image using a 5x5 average filter and then I am supposed to deblur using an Inverse filter using Constrained Division. Is there a better way to do this?

The error that pops up is this:

 Error using  ./ 
 Complex integer arithmetic is not supported.

 Error in test2 (line 12)
 fbw = fftshift(fft2(blur))./bw;

When I blur the image using a butterworth filter it works just fine yet when I blur using the average filter my code won't work. Below is my attempt:

 clear
 I = imread('buffalo.png');
 H = fspecial('average',[5 5]) ;
 blurred=imfilter(I,H);
 blur=im2uint8(mat2gray((blurred)));
 imshow(blur)

 d = 0.01;           
 bw = blurred;        
 bw(find(bw<d)) = 1; 
 fbw = fftshift(fft2(blur))./bw; 
 ba = abs(ifft2(fbw)); 
 unb01 = im2uint8(mat2gray(ba));

Answer 1

im=imread('Sharon2.jpg');

y=rgb2gray(im);

z=imresize(y,[512 512]);

x=imrotate(z,-270);

figure,

imshow(x);

% Define blur kernel

hsize = 17;

sigma = 5; % Whatever

kernel = fspecial('gaussian', hsize, sigma)

% Read in sample image.

grayImage = imread('cameraman.tif'); % Sample image.

[rows, columns, numberOfColorChannels] = size(grayImage)

w = floor(hsize/2);

for col = w+1 : columns - w

for row = w+1 : rows - w

% code to filter one pixel at location (row, col)

% Get a window around the target pixel that we

% will consider when blurring.

row1 = row - w;

row2 = row1 + hsize - 1;

col1 = col - w;

col2 = col1 + hsize - 1;

subImage = double(grayImage(row1:row2, col1:col2));

% Blur the pixel at row, column.

tempImage = subImage .* kernel;

newPixelValue = uint8(sum(tempImage(:)));

% Put blurred value back into original image

grayImage(row, col) = newPixelValue;

end

end

imshow(grayImage, []);

title('Blurred Image', 'FontSize', 30);

% Enlarge figure to full screen.

set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);

% Give a name to the title bar.

set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')

grayImage = imread('Sharon2.jpg'); % Sample image.

z=imresize(grayImage,[512 512]);

x=imrotate(z,-270);

y=rgb2gray(x);

figure,

imshow(y);

% Define blur kernel

hsize = 17;

sigma = 5; % Whatever

kernel = fspecial('gaussian', hsize, sigma);

% Read in sample image.

grayImage = imread('Sharon2.jpg'); % Sample image.

z=imresize(grayImage,[512 512]);

x=imrotate(z,-270);

y=rgb2gray(x);

figure,

imshow(y);

% Get a window around the target pixel that we

% will consider when blurring.

row1 = 100 - floor(hsize/2);

row2 = row1 + hsize - 1;

col1 = 150 - floor(hsize/2);

col2 = col1 + hsize - 1;

subImage = double(grayImage(row1:row2, col1:col2));

% Blur the pixel at row 100, column 150.

tempImage = subImage .* kernel;

newPixelValue = uint8(sum(tempImage(:)));

% Put blurred value back into original image

grayImage(100, 150) = newPixelValue;

figure,

imshow(tempImage);

% for col = 1 : columns

% for row = 1 : rows

% % code to filter one pixel at location (row, col)

% end

% end