mlsanityData to 7 Oct 2026

Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior

arXiv 2305.07712 ↗First posted 12 May 2023eess.SP cs.AIcited by 1

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements are affected by a combination of Poisson and Gaussian noise, which commonly occurs in optical imaging systems. To address this problem, we propose a new algorithm called "AWFS" that uses the accelerated Wirtinger flow (AWF) with a score function as generative prior. Specifically, we formulate the PR problem as an optimization problem that incorporates both data fidelity and regularization terms. We calculate the gradient of the log-likelihood function for PR and determine its corresponding Lipschitz constant. Additionally, we introduce a generative prior in our regularization framework by using score matching to capture information about the gradient of image prior distributions. We provide theoretical analysis that establishes a critical-point convergence guarantee for the proposed algorithm. The results of our simulation experiments on three different datasets show the following: 1) By using the PG likelihood model, the proposed algorithm improves reconstruction compared to algorithms based solely on Gaussian or Poisson likelihood. 2) The proposed score-based image prior method, performs better than the method based on denoising diffusion probabilistic model (DDPM), as well as plug-and-play alternating direction method of multipliers (PnP-ADMM) and regularization by denoising (RED).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 56 of its 119 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

56 of 119 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022DOLPH: Diffusion Models for Phase Retrieval1 Nov
  2. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  3. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraints2 Jun

Other references · 53

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  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
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38 more
  1. 2021Score-based diffusion models for accelerated MRIcited by 308
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Citing papers per year: 2024: 1.

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  1. 2024Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problemscited by 8

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  6. 2025DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models
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