Convolutions in image processing | Week 1 | MIT Fall 2020 | Grant Sanderson

The basics of convolutions in the context of image processing. Course website: Contents: 0:00 - Introduction 1:12 - Box blur as an average 3:00 - Dealing with the edges 4:31 - Gaussian blur 5:30 - Visualizing gaussian blur 6:04 - Convolution 6:40 - Kernels and the gaussian kernel 7:26 - Looking at the convolution in Julia 8:45 - Julia: `ImageFiltering` package and Kernels 9:08 - Julia: `OffsetArray` with different indices 10:15 - Visualizing a kernel 11:25 - Computational complexity 12:00 - Julia: `prod` function for a product 13:00 - Example of a non-blurring kernel 16:00 - Sharpening edges in an image 17:13 - Edge detection with Sobel filters 21:25 - Relation to polynomial multiplication 25:00 - Convolution in polynomial multiplication 26:08 - Relation to Fourier transforms 28:50 - Fourier transform of an image 31:50 - Convolution via Fourier transform is faster 34:00 - Final thoughts To learn more about Julia, head to