mlsanityData to 7 Oct 2026

Phase recovery and holographic image reconstruction using deep learning in neural networks

arXiv 1705.04286 ↗First posted 10 May 2017cs.CV cs.IR cs.LGcited by 54

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Abstract (arXiv)

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. Here we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference related spatial artifacts. Compared to existing approaches, this neural network based method is significantly faster to compute, and reconstructs improved phase and amplitude images of the objects using only one hologram, i.e., requires less number of measurements in addition to being computationally faster. We validated this method by reconstructing phase and amplitude images of various samples, including blood and Pap smears, and tissue sections. These results are broadly applicable to any phase recovery problem, and highlight that through machine learning challenging problems in imaging science can be overcome, providing new avenues to design powerful computational imaging systems.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2017.

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Citing papers per year: 2021: 14; 2022: 11; 2023: 8; 2024: 13; 2025: 5; 2026: 3.

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  1. 2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28
  2. 2022Diffractive all-optical computing for quantitative phase imagingcited by 22
  3. 2023On the use of deep learning for phase recoverycited by 20
  4. 2021AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imagingcited by 18
  5. 2022Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalizationcited by 18
  6. 2022Self-supervised learning of hologram reconstruction using physics consistencycited by 14
  7. 2021FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imagingcited by 13
  8. 2021Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networkscited by 10
  9. 2023Multispectral Quantitative Phase Imaging Using a Diffractive Optical Networkcited by 10
  10. 2023eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imagingcited by 8
  11. 2022Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Networkcited by 7
  12. 2021Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral datacited by 6
38 more
  1. 2022Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networkscited by 6
  2. 2022Rapid and stain-free quantification of viral plaque via lens-free holography and deep learningcited by 6
  3. 2021Uncertainty quantification for ptychography using normalizing flowscited by 4
  4. 2021Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetworkcited by 3
  5. 2021Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samplescited by 3
  6. 2021SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Networkcited by 3
  7. 2021Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projectioncited by 3
  8. 2022Fast Autofocusing using Tiny Transformer Networks for Digital Holographic Microscopycited by 3
  9. 2023Stochastic Light Field Holographycited by 3
  10. 2024Predicting ptychography probe positions using single-shot phase retrieval neural networkcited by 3
  11. 2021Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximationcited by 2
  12. 2022Physics-informed neural networks for diffraction tomographycited by 2
  13. 2023Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imagingcited by 2
  14. 2023CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attentioncited by 2
  15. 2024All-optical complex field imaging using diffractive processorscited by 2
  16. 2024Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processorcited by 2
  17. 2024Hybrid deep learning and physics-based neural network for programmable illumination computational microscopycited by 2
  18. 2024Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximationcited by 2
  19. 2022Differentiable Microscopy Designs an All Optical Phase Retrieval Microscopecited 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. 2024Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss for Untrained Physics-Driven Deep Learning Models with Uncertain Distancecited by 1
  23. 2024Deep learning phase recovery: data-driven, physics-driven, or combining both?cited by 1
  24. 2024Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learningcited by 1
  25. 2024Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural networkcited by 1
  26. 2021Advances in Electron Microscopy with Deep Learningcited by 0
  27. 2021Dynamic imaging and characterization of volatile aerosols in e-cigarette emissions using deep learning-based holographic microscopycited by 0
  28. 2021Fast Computational Ghost Imaging using Unpaired Deep Learning and a Constrained Generative Adversarial Networkcited by 0
  29. 2021Lensless multicore-fiber microendoscope for real-time tailored light field generation with phase encoder neural network (CoreNet)cited by 0
  30. 2022DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging using Digital Holographycited by 0
  31. 2022Virtual impactor-based label-free bio-aerosol detection using holography and deep learningcited by 0
  32. 20243D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imagingcited by 0
  33. 2024Physics-informed generative real-time lens-free imagingcited by 0
  34. 2024UMSPU: Universal Multi-Size Phase Unwrapping via Mutual Self-Distillation and Adaptive Boosting Ensemble Segmenterscited by 0
  35. 2025PtyGenography: using generative models for regularization of the phase retrieval problemcited by 0
  36. 2025RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runscited by 0
  37. 2025DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learningcited by 0
  38. 2025Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologramcited by 0

50 most cited of 54 All 54 (JSON)