mlsanityData to 14 Oct 2026

On the use of deep learning for phase recovery

arXiv 2308.00942 ↗First posted 2 Aug 2023physics.optics cs.LG eess.IVcited by 20

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Abstract (arXiv)

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 89 of its 377 references that are in the map. First posted on arXiv 2 Aug 2023.

Builds on

89 of 377 references.

References · 89

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38 more
  1. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 307
  2. 2018All-Optical Machine Learning Using Diffractive Deep Neural Networkscited by 177
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50 most cited of 89 All 89 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 8; 2026: 4.

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  1. 2024All-optical complex field imaging using diffractive processorscited by 2
  2. 2025DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Modelscited by 2
  3. 2024Low-light phase retrieval with implicit generative priorscited by 1
  4. 2024Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Modelscited by 1
  5. 2024Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural networkcited by 1
  6. 2025Towards generalizable deep ptychography neural networkscited by 1
  7. 2025RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physicscited by 1
  8. 2026Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstructioncited by 1
  9. 2023Single-shot Phase Retrieval from a Fractional Fourier Transform Perspectivecited by 0
  10. 2024Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstructioncited by 0
  11. 2024Artificial intelligence inspired freeform optics design: a reviewcited by 0
  12. 2024Single-Shot Phase Diversity Wavefront Sensing in Deep Turbulence via Metasurface Opticscited by 0
8 more
  1. 2025GUSLO: General and Unified Structured Light Optimizationcited by 0
  2. 2025Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Frameworkcited by 0
  3. 2025prNet: Data-Driven Phase Retrieval via Stochastic Refinementcited by 0
  4. 2025I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Modelscited by 0
  5. 2025Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologramcited by 0
  6. 2026Pupil Design for Computational Wavefront Estimationcited by 0
  7. 2026Contrast-invariant deep ptychography neural networkscited by 0
  8. 2026Real-Time Atomic-Resolution Electron Phase Imaging without Probe Calibration via Ptychography-Supervised Learningcited by 0