Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

What is Wrong with End-to-End Learning for Phase Retrieval? (2403.15448v1)

Published 18 Mar 2024 in eess.SP and cs.LG

Abstract: For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches are used to solve such problems, these intrinsic symmetries can cause substantial learning difficulties. In this paper, we explain how such difficulties arise and, more importantly, how to overcome them by preprocessing the training set before any learning, i.e., symmetry breaking. We take far-field phase retrieval (FFPR), which is central to many areas of scientific imaging, as an example and show that symmetric breaking can substantially improve data-driven learning. We also formulate the mathematical principle of symmetry breaking.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. The gap between theory and practice in function approximation with deep neural networks, 2021.
  2. Solving inverse problems using data-driven models. Acta Numerica, 28:1–174, may 2019.
  3. Fourier phase retrieval: Uniqueness and algorithms. In Compressed Sensing and its Applications, pages 55–91. Springer International Publishing, 2017.
  4. Real-time coherent diffraction inversion using deep generative networks. Scientific Reports, 8(1), nov 2018.
  5. Egocentric scene understanding via multimodal spatial rectifier. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2822–2831, 2022.
  6. Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 18105–18114, June 2023.
  7. J. R. Fienup. Phase retrieval algorithms: a comparison. Applied Optics, 21(15):2758, aug 1982.
  8. Low photon count phase retrieval using deep learning. Physical Review Letters, 121(24), dec 2018.
  9. M. Hayes. The reconstruction of a multidimensional sequence from the phase or magnitude of its fourier transform. IEEE Transactions on Acoustics, Speech, and Signal Processing, 30(2):140–154, apr 1982.
  10. Learning-based imaging through scattering media. Optics Express, 24(13):13738, jun 2016.
  11. Framenet: Learning local canonical frames of 3d surfaces from a single rgb image. In Proceedings of the IEEE International Conference on Computer Vision, pages 8638–8647, 2019.
  12. Deep iterative reconstruction for phase retrieval. Applied Optics, 58(20):5422, jul 2019.
  13. Phase retrieval: An overview of recent developments, 2015.
  14. Phase extraction neural network (phenn) with coherent modulation imaging (cmi) for phase retrieval at low photon counts. Opt. Express, 28(15):21578–21600, Jul 2020.
  15. hp-vpinns: Variational physics-informed neural networks with domain decomposition. Computer Methods in Applied Mechanics and Engineering, 374:113547, 2021.
  16. Deep non-rigid structure from motion. In Proceedings of the IEEE International Conference on Computer Vision, pages 1558–1567, 2019.
  17. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5(10):1181, sep 2018.
  18. Physics-informed neural networks with hard constraints for inverse design, 2021.
  19. Using deep neural networks for inverse problems in imaging: Beyond analytical methods. IEEE Signal Processing Magazine, 35(1):20–36, jan 2018.
  20. A novel complex-valued blind source separation and its applications in integrated reception. Electronics, 12(18), 2023.
  21. Admm based fourier phase retrieval with untrained generative prior, 2022.
  22. X-ray image reconstruction from a diffraction pattern alone. Physical Review B, 68(14):140101, 2003.
  23. Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Processing Magazine, 34(6):85–95, nov 2017.
  24. Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging. Optica, 7(1):63, jan 2020.
  25. prdeep: Robust phase retrieval with a flexible deep network. arXiv preprint arXiv:1803.00212, 2018.
  26. Learning two-phase microstructure evolution using neural operators and autoencoder architectures, 2022.
  27. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, 2019.
  28. Phase retrieval with application to optical imaging: A contemporary overview. IEEE Signal Processing Magazine, 32(3):87–109, may 2015.
  29. Lensless computational imaging through deep learning. Optica, 4(9):1117, sep 2017.
  30. Scale-recurrent network for deep image deblurring. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, jun 2018.
  31. Inverse problems, deep learning, and symmetry breaking. arXiv preprint arXiv:2003.09077, 2020.
  32. Unlocking inverse problems using deep learning: Breaking symmetries in phase retrieval. In NeurIPS 2020 Workshop on Deep Learning and Inverse Problems, 2020.
  33. Phase retrieval using conditional generative adversarial networks. arXiv:1912.04981, 2019.
  34. Deep nrsfm++: Towards 3d reconstruction in the wild. arXiv:2001.10090, 2020.
  35. Sisprnet: end-to-end learning for single-shot phase retrieval. Optics Express, 30(18):31937, August 2022.
  36. Kbnet: Kernel basis network for image restoration. arXiv preprint arXiv:2303.02881, 2023.
  37. Phasegan: a deep-learning phase-retrieval approach for unpaired datasets. Opt. Express, 29(13):19593–19604, Jun 2021.
  38. Practical phase retrieval using double deep image priors, 2022.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now