A little art and plenty of science. The kernel matrices can be broken down logically once you know what the numbers are operating on. Considering a 3x3 kernel,

A little art and plenty of science. The kernel matrices can be broken down logically once you know what the numbers are operating on. Considering a 3x3 kernel, the center of the kernel matrix is the origin pixel, and the kernel elements around the origin are the neighboring pixels in their respective directions and distances. The identity kernel is [0 0 0; 0 1 0; 0 0 0]. For every input pixel, the output is the original pixel value. Don't change the pixel value based on what is around it. A simple blur kernel would be 1/9 * [1 1 1; 1 1 1; 1 1 1]. The output for each input pixel is the average of the origin's pixel value and all of its neighboring values, with an even weighting. A less dramatic blur can weigh the origin pixel higher than the neighbors, such as a gaussian blur. This will result in the output pixel being more similar to the origin pixel, than to it's neighbors. Edge detection like [-1 -1 -1; -1 8 -1; -1 -1 -1] can be understood by multiplying the origin pixel value by 8, and subtracting off all of its 8 neighboring values. If the values are all fairly similar, lets say all gray, your output will be black. 8A - 8A = 0. So it "punishes" pixels that are similar to its neighbors. When a pixel is different than some or all of its neighbors, you will be left with some value > 0 at the output, which detects a change from its neighbors: an edge. Horizontal edge detection: [1 0 -1; 2 0 -2; 1 0 -1]. Ignores the pixels above, below, and center, but accentuates the differences between what is on the left from the right. https://en.wikipedia.org/wiki/Kernel_(image_processing) https://en.wikipedia.org/wiki/Kernel_(image_processing)