Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval

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...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors: Agrawal, Rohun, Leong, Oscar
Format: Conference Proceeding
Language:English
Published: IEEE 04.06.2023
Subjects:
Alternating Minimization
Denoiser
Extraterrestrial measurements
Image Prior
Inverse problems
Microscopy
Minimization
Phase measurement
Phase Retrieval
Signal processing
Signal processing algorithms
ISSN:2379-190X
Online Access:Get full text
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096782