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Title: Deep Expectation-Consistent Approximation for Phase Retrieval
The expectation consistent (EC) approximation framework is a state-of-the-art approach for solving (generalized) linear inverse problems with high-dimensional random forward operators and i.i.d. signal priors. In image inverse problems, however, both the forward operator and image pixels are structured, which plagues traditional EC implementations. In this work, we propose a novel incarnation of EC that exploits deep neural networks to handle structured operators and signals. For phase-retrieval, we propose a simplified variant called “deepECpr” that reduces to iterative denoising. In experiments recovering natural images from phaseless, shot-noise corrupted, coded-diffraction-pattern measurements, we observe accuracy surpassing the state- of-the-art prDeep (Metzler et al., 2018) and Diffusion Posterior Sampling (Chung et al., 2023) approaches with two-orders-of- magnitude complexity reduction.  more » « less
Award ID(s):
1955587
PAR ID:
10536542
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-2574-4
Page Range / eLocation ID:
910 to 914
Format(s):
Medium: X
Location:
Pacific Grove, CA, USA
Sponsoring Org:
National Science Foundation
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