working with image processing makes convolution easily intuitive. you can get a "feel" of what the math is doing by looking what's happening with the images. wh

working with image processing makes convolution easily intuitive. you can get a "feel" of what the math is doing by looking what's happening with the images. while blurring is a common example (convolve a Gaussian function with the image), edge detection and other image operations are just a matter of changing the convolved signal (or kernel). in a simplified model, blurry photos due to poor lenses also share the same principle (the lens cannot form a perfect point, that imperfect point is effectively what is convolved). the same can be said for audio and other 1d data, but images are more visual.