better edge enhancement how?

any suggestion how to enhace the image edges clearly?

I = imread('317.jpg');

% A = rgb2gray(I2);

figure;

imshow(I)

title('Original Image')

%%

%Gaussian LPF

F = fspecial('gaussian');

G = imfilter(I,F);

figure(5),imshow(G);title('G Image');

%%

bw = im2bw(G,0.35);

figure(4),imshow(bw)

kernel = -1*ones(1);

kernel(2,2) = 4;

enhancedImage = imfilter(bw, kernel);

figure(6),imshow(enhancedImage);title('enhancedImage Image');

D = im2uint8(enhancedImage);

I2 = imcrop(D,[50 68 130 112]);

figure;

imshow(I2)

r = drawrectangle;

mask = createMask(r);

bw2 = activecontour(I2,mask,30,'Chan-Vese');

hold on;

visboundaries(bw2,'Color','r');

figure

imshow(labeloverlay(I2,bw2));

imshow(bw2);

Answer 1

[Vidhi Agarwal]

Hi @MOHAMED GILANI

I understand that you have a query about enhancing the edges of an image. Here are a few suggestions for the same:

1. Ensure that the image is converted to grayscale before applying any edge detection techniques. This simplifies the processing, and it is a standard practice for edge detection.
2. Instead of using a simple Laplacian kernel, you can use methods like the Canny edge detector for better results.
3. The kernel you defined seems incorrect. If you want to use a Laplacian kernel, it should be a 3x3 matrix. Consider using “fspecial('laplacian')” for a standard Laplacian filter.
4. Consider using unsharp masking for edge enhancement, which combines the original image with a high-pass filtered version.

Here’s is the revised code for the same:

I = imread('317.jpg');

% Convert to grayscale
I_gray = rgb2gray(I);

% Display original grayscale image
figure;
imshow(I_gray);
title('Original Grayscale Image');

% Apply Gaussian Low Pass Filter
F = fspecial('gaussian', [5 5], 1); % Adjust size and sigma as needed
G = imfilter(I_gray, F, 'same');
figure;
imshow(G);
title('Gaussian Blurred Image');

% Edge Detection using Canny
edges = edge(G, 'canny');
figure;
imshow(edges);
title('Canny Edge Detection');

% Optional: Enhance edges using Unsharp Masking
H = fspecial('unsharp');
enhancedImage = imfilter(I_gray, H);
figure;
imshow(enhancedImage);
title('Enhanced Image with Unsharp Masking');

% Convert to binary image for segmentation
bw = imbinarize(enhancedImage, 'adaptive', 'Sensitivity', 0.35);
figure;
imshow(bw);
title('Binary Image');

% Crop and apply active contour model
D = im2uint8(bw);
I2 = imcrop(D, [50 68 130 112]);
figure;
imshow(I2);
title('Cropped Image');

% Draw rectangle for initial mask
r = drawrectangle;
mask = createMask(r);
bw2 = activecontour(I2, mask, 30, 'Chan-Vese');
hold on;
visboundaries(bw2, 'Color', 'r');

% Display the final result
figure;
imshow(labeloverlay(I2, bw2));
title('Segmented Image with Boundaries');

The output of the following code will look like:

For better understanding of grayscale images and conversion to it, Canny edge detection and “fspecial” function, refer to the following documentation:

• https://www.mathworks.com/help/matlab/ref/rgb2gray.html
• https://www.mathworks.com/help/images/edge-detection.html
• https://www.mathworks.com/help/images/ref/fspecial.html

Hope that Helps!.