mlsanityData to 7 Oct 2026

DOLPH: Diffusion Models for Phase Retrieval

arXiv 2211.00529 ↗First posted 1 Nov 2022eess.IV cs.CVcited by 4

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

Abstract (arXiv)

Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires prior knowledge on the unknown image. We present DOLPH as a new deep model-based architecture for phase retrieval that integrates an image prior specified using a diffusion model with a nonconvex data-fidelity term for phase retrieval. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLPH reconstructs high-quality solutions by alternating data-consistency updates with the sampling step of a diffusion model. Our numerical results show the robustness of DOLPH to noise and its ability to generate several candidate solutions given a set of measurements.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 14 of its 26 references that are in the map. First posted on arXiv 1 Nov 2022.

Builds on

14 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraints2 Jun

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  4. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
  5. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  6. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  8. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  9. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 275
  10. 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
  11. 2013Phase Retrieval from Coded Diffraction Patternscited by 26
  12. 2019Online Regularization by Denoising with Applications to Phase Retrievalcited by 12
1 more
  1. 2017PhasePack: A Phase Retrieval Librarycited by 4

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2024Posterior Sampling with Denoising Oracles via Tilted Transportcited by 12
  2. 2023Diffusion Posterior Sampling for Informed Single-Channel Dereverberationcited by 9
  3. 2023Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Priorcited by 1
  4. 2025Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurementscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Models
  2. 2025DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models
  3. 2023Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging
  4. 2023Reflected Diffusion Models
  5. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Models
  6. 2025Learning Single Index Models with Diffusion Priors
  7. 2024A Survey on Diffusion Models for Inverse Problems
  8. 2023Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision