We use different types of kernels for sharpening and blurring images. The kernel for sharpening (the sharpen kernel) highlights the differences in adjacent pixel values, which emphasizes detail by enhancing contrast.
We will look at different examples of sharpening by multiplying the image pixels by 9 or 5 kernels and the other pixels around them by -1 or 0, as shown in the following matrix. The sharpening kernel is simply a way of enhancing the pixel of the image at any point.
Sharpening kernel type 1:
Sharpening kernel type 2:
Next, we will look at blurring kernels.
A blurring kernel is used to blur an image by averaging each pixel value and its neighbors. The blurring kernel is an N x N matrix filled with ones. Normalization has to be performed to achieve blurring. The values in the matrix have to collectively total to 1. If the sum doesn't add up to 1, then the image will be brighter or darker, as shown in Fig 4.36...
