Phase Retrieval using Untrained Neural Network Priors

Gauri Jagatap, Chinmay Hegde

Abstract: Untrained deep neural networks as image priors have been recently introduced for linear inverse imaging problems such as denoising, super-resolution, inpainting and compressive sensing with promising performance gains over hand-crafted image priors such as sparsity. Moreover, unlike learned generative priors they do not require any training over large datasets. In this paper, we consider the problem of solving the non-linear inverse problem of compressive phase retrieval; this involves reconstructing a $d$-dimensional image signal from $n$ magnitude-only measurements, and $n