One Network to Solve Them All — Solving Linear Inverse Problems using Deep Projection Models

We propose a general framework to train a single deep neural network that solves arbitrary linear inverse problems. The proposed network acts as a proximal operator for an optimization algorithm and projects non-image signals onto the set of natural images defined by the decision boundary of a classifier.