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Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval by Rohun Agrawal; Oscar Leong is a scholarly article available to read on EtoBox.

What is Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval about?

Phase retrieval is the nonlinear inverse problem of recovering a true signal from its Fourier magnitude measurements. It arises in many applications such as astronomical imaging, X-Ray crystallography, microscopy, and more. The problem is highly ill-posed due to the phase-induced ambiguities and the large number of possible images that can fit to the given measurements. Thus, there's a rich history of enforcing structural priors to improve solutions including sparsity priors and deep-learning-based generative models. However, such priors are often limited in their representational capacity or generalizability to slightly different distributions. Recent advancements in using denoisers as regularizers for non-convex optimization algorithms have shown promising performance and generalization. We present a way of leveraging the prior implicitly learned by a denoiser to solve phase retrieval problems by incorporating it in a classical alternating minimization framework. Compared to performant denoising-based algorithms for phase retrieval, we showcase competitive performance with Fourier measurements on in-distribution images and notable improvement on out-of-distribution images.

Author

Rohun Agrawal; Oscar Leong

Publisher

IEEE

Published

2023

Language

EN