mlsanityData to 14 Oct 2026

prDeep: Robust Phase Retrieval with a Flexible Deep Network

arXiv 1803.00212 ↗First posted 1 Mar 2018stat.ML cs.LGcited by 47

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Abstract (arXiv)

Phase retrieval algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation imaging, they enable imaging past the diffraction limit and through scattering media, respectively. Unfortunately, traditional phase retrieval algorithms struggle in the presence of noise. Progress has been made recently on more robust algorithms using signal priors, but at the expense of limiting the range of supported measurement models (e.g., to Gaussian or coded diffraction patterns). In this work we leverage the regularization-by-denoising framework and a convolutional neural network denoiser to create prDeep, a new phase retrieval algorithm that is both robust and broadly applicable. We test and validate prDeep in simulation to demonstrate that it is robust to noise and can handle a variety of system models. A MatConvNet implementation of prDeep is available at https://github.com/ricedsp/prDeep.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Mar 2018.

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Citing papers per year: 2021: 7; 2022: 12; 2023: 11; 2024: 7; 2025: 7; 2026: 3.

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  1. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 108
  2. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 97
  3. 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstructioncited by 74
  4. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  5. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  6. 2023On the use of deep learning for phase recoverycited by 20
  7. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  8. 2024Plug-and-Play image restoration with Stochastic deNOising REgularizationcited by 17
  9. 2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 11
  10. 2022Practical Phase Retrieval Using Double Deep Image Priorscited by 10
  11. 2022Deep Model-Based Architectures for Inverse Problems under Mismatched Priorscited by 8
  12. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
35 more
  1. 2023Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Modelscited by 8
  2. 2021Non-Iterative Phase Retrieval With Cascaded Neural Networkscited by 6
  3. 2021Dynamic Proximal Unrolling Network for Compressive Imagingcited by 4
  4. 2022DOLPH: Diffusion Models for Phase Retrievalcited by 4
  5. 2021Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetworkcited by 3
  6. 2022Monotonically Convergent Regularization by Denoisingcited by 3
  7. 2022Deep Iterative Phase Retrieval for Ptychographycited by 3
  8. 2022Complex-valued Retrievals From Noisy Images Using Diffusion Modelscited by 3
  9. 2023PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrievalcited by 3
  10. 2024Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron laserscited by 3
  11. 2024Plug-and-Play Half-Quadratic Splitting for Ptychographycited by 3
  12. 2023RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imagingcited by 2
  13. 2024What is Wrong with End-to-End Learning for Phase Retrieval?cited by 2
  14. 2025DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Modelscited by 2
  15. 2025RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imagingcited by 2
  16. 2025Plug-and-Play Posterior Sampling for Blind Inverse Problemscited by 2
  17. 2022PR-DAD: Phase Retrieval Using Deep Auto-Decoderscited by 1
  18. 2022Optimizing Intermediate Representations of Generative Models for Phase Retrievalcited by 1
  19. 2022Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrievalcited by 1
  20. 2023SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problemscited by 1
  21. 2024Low-light phase retrieval with implicit generative priorscited by 1
  22. 2024Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Modelscited by 1
  23. 2021Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functionscited by 0
  24. 2022Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Networkcited by 0
  25. 2023Untrained neural network embedded Fourier phase retrieval from few measurementscited by 0
  26. 2023Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Datacited by 0
  27. 2023Single-shot Phase Retrieval from a Fractional Fourier Transform Perspectivecited by 0
  28. 2023Coordinate-based Neural Network for Fourier Phase Retrievalcited by 0
  29. 2025prNet: Data-Driven Phase Retrieval via Stochastic Refinementcited by 0
  30. 2025I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Modelscited by 0
  31. 2025Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologramcited by 0
  32. 2025DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrievalcited by 0
  33. 2026A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychographycited by 0
  34. 2026Pupil Design for Computational Wavefront Estimationcited by 0
  35. 2026Deeply unrolled alternating projections for phase retrievalcited by 0