Laplacian Image Filtering and Sharpening Images in MATLAB

Question

I am trying to \"translate\" what\'s mentioned in Gonzalez and Woods (2nd Edition) about the Laplacian filter.

I\'ve read in the image and created the filter. However, w

Answer 1

I have a few tips for you:

1. This is just a little thing but filter2 performs correlation. You actually need to perform convolution, which rotates the kernel by 180 degrees before performing the weighted sum between neighbourhoods of pixels and the kernel. However because the kernel is symmetric, convolution and correlation perform the same thing in this case.
2. I would recommend you use imfilter to facilitate the filtering as you are using methods from the Image Processing Toolbox already. It's faster than filter2 or conv2 and takes advantage of the Intel Integrated Performance Primitives.
3. I highly recommend you do everything in double precision first, then convert back to uint8 when you're done. Use im2double to convert your image (most likely uint8) to double precision. When performing sharpening, this maintains precision and prematurely casting to uint8 then performing the subtraction will give you unintended side effects. uint8 will cap results that are negative or beyond 255 and this may also be a reason why you're not getting the right results. Therefore, convert the image to double, filter the image, sharpen the result by subtracting the image with the filtered result (via the Laplacian) and then convert back to uint8 by im2uint8.

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You've also provided a link to the pipeline that you're trying to imitate: http://www.idlcoyote.com/ip_tips/sharpen.html

The differences between your code and the link are:

1. The kernel has a positive centre. Therefore the 1s are negative while the centre is +8 and you'll have to add the filtered result to the original image.
2. In the link, they normalize the filtered response so that the minimum is 0 and the maximum is 1.
3. Once you add the filtered response onto the original image, you also normalize this result so that the minimum is 0 and the maximum is 1.
4. You perform a linear contrast enhancement so that intensity 60 becomes the new minimum and intensity 200 becomes the new maximum. You can use imadjust to do this. The function takes in an image as well as two arrays - The first array is the input minimum and maximum intensity and the second array is where the minimum and maximum should map to. As such, I'd like to map the input intensity 60 to the output intensity 0 and the input intensity 200 to the output intensity 255. Make sure the intensities specified are between 0 and 1 though so you'll have to divide each quantity by 255 as stated in the documentation.

As such:

clc;
close all;
a = im2double(imread('moon.png')); %// Read in your image
lap = [-1 -1 -1; -1 8 -1; -1 -1 -1]; %// Change - Centre is now positive
resp = imfilter(a, lap, 'conv'); %// Change

%// Change - Normalize the response image
minR = min(resp(:));
maxR = max(resp(:));
resp = (resp - minR) / (maxR - minR);

%// Change - Adding to original image now
sharpened = a + resp;

%// Change - Normalize the sharpened result
minA = min(sharpened(:));
maxA = max(sharpened(:));
sharpened = (sharpened - minA) / (maxA - minA);

%// Change - Perform linear contrast enhancement
sharpened = imadjust(sharpened, [60/255 200/255], [0 1]);

figure; 
subplot(1,3,1);imshow(a); title('Original image');
subplot(1,3,2);imshow(resp); title('Laplacian filtered image');
subplot(1,3,3);imshow(sharpened); title('Sharpened image');

I get this figure now... which seems to agree with the figures seen in the link: