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Coordinate-based Neural Network for Fourier Phase Retrieval

arXiv 2311.14925 ↗First posted 25 Nov 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

Fourier phase retrieval is essential for high-definition imaging of nanoscale structures across diverse fields, notably coherent diffraction imaging. This study presents the Single impliCit neurAl Network (SCAN), a tool built upon coordinate neural networks meticulously designed for enhanced phase retrieval performance. Remedying the drawbacks of conventional iterative methods which are easiliy trapped into local minimum solutions and sensitive to noise, SCAN adeptly connects object coordinates to their amplitude and phase within a unified network in an unsupervised manner. While many existing methods primarily use Fourier magnitude in their loss function, our approach incorporates both the predicted magnitude and phase, enhancing retrieval accuracy. Comprehensive tests validate SCAN's superiority over traditional and other deep learning models regarding accuracy and noise robustness. We also demonstrate that SCAN excels in the ptychography setting.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 25 Nov 2023.

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16 of 33 references.

References · 16

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  2. 2017Deep Image Priorcited by 1,097
  3. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  4. 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
  5. 2023WIRE: Wavelet Implicit Neural Representationscited by 220
  6. 2018Phase Retrieval Under a Generative Priorcited by 73
  7. 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
  8. 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
  9. 2021AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imagingcited by 18
  10. 2018Real-time coherent diffraction inversion using deep generative networkscited by 17
  11. 2021Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networkscited by 10
  12. 2022Practical Phase Retrieval Using Double Deep Image Priorscited by 10
4 more
  1. 2020Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imagingcited by 8
  2. 2020DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrievalcited by 8
  3. 2022SiSPRNet: End-to-End Learning for Single-Shot Phase Retrievalcited by 7
  4. 2023FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representationscited by 4

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