This document discusses inverse problems in imaging such as inpainting, deblurring, superresolution, compressed sensing, MRI, and radar. It notes that inverse problems involve observing data y that is related to the desired signal β through a model y = Xβ + ε, and the goal is to recover β from the observed data y. One classical approach is Tikhonov regularization from 1943, which finds the least squares solution to deblurring problems. The document provides examples of how deblurring an image the "naive" way can lead to wildly different solutions compared to using Tikhonov regularization.