Implementing Convolutions with Python:-
Convolution is simply the sum of element-wise matrix multiplication between the kernel and neighborhood that the kernel covers of the input image.
#Kernel:
We use an odd kernel size to ensure there is a valid integer (x;y)-coordinate at the center of the image. On the left, we have a 3X3 matrix. The center of the matrix is located at x = 1;y = 1 where the top-left corner of the matrix is used as the origin and our coordinates are zero-indexed.
But on the right, we have a 2X2 matrix. The center of this matrix would be located at x = 0:5;y = 0:5. But as we know, without applying interpolation, there is no such thing as pixel location (0:5;0:5) – our pixel coordinates must be integers!
This reasoning is exactly why we use odd kernel sizes: to always ensure there is a valid (x;y)-coordinate at the center of the kernel.
This are the kernel which we had used in our code
Sharpen kernel responsible for sharpening an image.
Laplacian kernel used to detect edge-like regions.
Sobel kernels can be used to detect edge-like regions along both the x and y axis respectively.
sobelX kernel is used to find vertical edges in the image, while the sobelY kernel reveals horizontal edges.