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The Numerics of Phase Retrieval

arXiv 2004.05788 ↗First posted 13 Apr 2020eess.IV cs.NA math.NAcited by 20

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Abstract (arXiv)

Phase retrieval, i.e., the problem of recovering a function from the squared magnitude of its Fourier transform, arises in many applications such as X-ray crystallography, diffraction imaging, optics, quantum mechanics, and astronomy. This problem has confounded engineers, physicists, and mathematicians for many decades. Recently, phase retrieval has seen a resurgence in research activity, ignited by new imaging modalities and novel mathematical concepts. As our scientific experiments produce larger and larger datasets and we aim for faster and faster throughput, it becomes increasingly important to study the involved numerical algorithms in a systematic and principled manner. Indeed, the last decade has witnessed a surge in the systematic study of computational algorithms for phase retrieval. In this paper we will review these recent advances from a numerical viewpoint.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 4; 2024: 5; 2025: 5.

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  1. 2021Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problemcited by 53
  2. 2021Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passingcited by 18
  3. 2023From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regressioncited by 16
  4. 2022Practical Phase Retrieval Using Double Deep Image Priorscited by 10
  5. 2023Image Reconstruction without Explicit Priorscited by 8
  6. 2023Discovering Structure From Corruption for Unsupervised Image Reconstructioncited by 5
  7. 2023Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passingcited by 5
  8. 2024Stable Phase Retrieval with Mirror Descentcited by 3
  9. 2022Provable Phase Retrieval with Mirror Descentcited by 2
  10. 2024Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximationcited by 2
  11. 2024Robust Gradient Descent for Phase Retrievalcited by 2
  12. 2025DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Modelscited by 2
8 more
  1. 2021WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditionscited by 1
  2. 2024Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantificationcited by 1
  3. 2025Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inferencecited by 1
  4. 2022Machine learning for classifying and interpreting coherent X-ray speckle patternscited by 0
  5. 2024Low Complexity Regularized Phase Retrievalcited by 0
  6. 2025Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappingscited by 0
  7. 2025prNet: Data-Driven Phase Retrieval via Stochastic Refinementcited by 0
  8. 2025I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Modelscited by 0