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Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction

arXiv 2404.06657 ↗First posted 9 Apr 2024eess.IV cs.CV physics.app-phcited by 0

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Abstract (arXiv)

Conventional deep learning-based image reconstruction methods require a large amount of training data which can be hard to obtain in practice. Untrained deep learning methods overcome this limitation by training a network to invert a physical model of the image formation process. Here we present a novel untrained Res-U2Net model for phase retrieval. We use the extracted phase information to determine changes in an object's surface and generate a mesh representation of its 3D structure. We compare the performance of Res-U2Net phase retrieval against UNet and U2Net using images from the GDXRAY dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 74 references that are in the map. First posted on arXiv 9 Apr 2024.

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22 of 74 references.

References · 22

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10 more
  1. 2018Fringe pattern analysis using deep learningcited by 13
  2. 2021Untrained networks for compressive lensless photographycited by 13
  3. 2021Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networkscited by 10
  4. 2020DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrievalcited by 8
  5. 2017Computational ghost imaging using deep learningcited by 7
  6. 2021Non-Iterative Phase Retrieval With Cascaded Neural Networkscited by 6
  7. 2016Phase Retrieval from 1D Fourier Measurements: Convexity, Uniqueness, and Algorithmscited by 3
  8. 2021Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetworkcited by 3
  9. 2015Analysis and approximation of some Shape-from-Shading models for non-Lambertian surfacescited by 2
  10. 2019Transfer Learning in General Lensless Imaging through Scattering Mediacited by 1

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