prDeep: Robust Phase Retrieval with a Flexible Deep Network

TL;DR

prDeep introduces a deep learning-based phase retrieval algorithm that is robust to noise and adaptable to various measurement models, advancing computational imaging capabilities.

Contribution

It leverages a denoising neural network within a regularization framework to create a flexible, noise-robust phase retrieval method applicable to multiple measurement models.

Findings

Demonstrates robustness to noise in simulations

Handles diverse measurement models effectively

Outperforms traditional algorithms in noisy conditions

Abstract

Phase retrieval algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation imaging, they enable imaging past the diffraction limit and through scattering media, respectively. Unfortunately, traditional phase retrieval algorithms struggle in the presence of noise. Progress has been made recently on more robust algorithms using signal priors, but at the expense of limiting the range of supported measurement models (e.g., to Gaussian or coded diffraction patterns). In this work we leverage the regularization-by-denoising framework and a convolutional neural network denoiser to create prDeep, a new phase retrieval algorithm that is both robust and broadly applicable. We test and validate prDeep in simulation to demonstrate that it is robust to noise and can handle a variety of system models. A MatConvNet implementation of prDeep is available at https://github.com/ricedsp/prDeep.