mlsanityData to 14 Oct 2026

Practical Phase Retrieval Using Double Deep Image Priors

arXiv 2211.00799 ↗First posted 2 Nov 2022cs.CV cs.LG eess.IVcited by 10

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Abstract (arXiv)

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 23 of its 56 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

23 of 56 references.

References · 23

  1. 2017Deep Image Priorcited by 1,098
  2. 2018"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priorscited by 114
  3. 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 47
  4. 2017Lensless computational imaging through deep learningcited by 44
  5. 2021Early Stopping for Deep Image Priorcited by 44
  6. 2019Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generatorscited by 40
  7. 2020Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationcited by 39
  8. 2020The Numerics of Phase Retrievalcited by 21
  9. 2021AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imagingcited by 18
  10. 2018Real-time coherent diffraction inversion using deep generative networkscited by 17
  11. 2020PhaseGAN: A deep-learning phase-retrieval approach for unpaired datasetscited by 15
  12. 2021Self-Validation: Early Stopping for Single-Instance Deep Generative Priorscited by 15
11 more
  1. 2015Experimental robustness of Fourier Ptychography phase retrieval algorithmscited by 14
  2. 2019Deep Iterative Reconstruction for Phase Retrievalcited by 14
  3. 2019Phase Retrieval Using Conditional Generative Adversarial Networkscited by 11
  4. 2021Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networkscited by 10
  5. 2022Blind Image Deblurring with Unknown Kernel Size and Substantial Noisecited by 10
  6. 2020Inverse Problems, Deep Learning, and Symmetry Breakingcited by 8
  7. 2020Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imagingcited by 8
  8. 2020When deep denoising meets iterative phase retrievalcited by 6
  9. 2021Phase Retrieval using Single-Instance Deep Generative Priorcited by 4
  10. 2021Towards Low-Photon Nanoscale Imaging: Holographic Phase Retrieval via Maximum Likelihood Optimizationcited by 3
  11. 2018Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisonscited by 1

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 4.

10 papers.

  1. 2025A Baseline Method for Removing Invisible Image Watermarks using Deep Image Priorcited by 7
  2. 2023JoIN: Joint GANs Inversion for Intrinsic Image Decompositioncited by 4
  3. 2024Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noisecited by 4
  4. 2025Multilook Coherent Imaging: Theoretical Guarantees and Algorithmscited by 4
  5. 2025FMPlug: Plug-In Foundation Flow-Matching Priors for Inverse Problemscited by 3
  6. 2023Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Priorcited by 1
  7. 2023Acceleration and Implicit Regularization in Gaussian Phase Retrievalcited by 1
  8. 2024Low-light phase retrieval with implicit generative priorscited by 1
  9. 2023Coordinate-based Neural Network for Fourier Phase Retrievalcited by 0
  10. 2025Saving Foundation Flow-Matching Priors for Inverse Problemscited by 0