kqwang/phase-recovery

• highreadme#1

  Reposition the README's opening to clearly state it's a curated resource list

  Why:

  CURRENT

  # Resources for phase recovery
  # Released from here
  Here, we refer to *“calculating the phase of a light field from its amplitude/intensity measurements”* as phase recovery (PR), which contains many techniques and algorithms, such as holography/interferometry, transport of intensity equation (TIE), phase retrieval (optimization-based approaches), wavefront sensing, and deep-learning-based approaches.

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  # A Curated List of Resources for Phase Recovery (Phase Imaging, Phase Retrieval, Phase Reconstruction)
  This repository provides a comprehensive, curated collection of resources, including papers, groups, companies, and workshops, related to phase recovery. It covers various techniques and algorithms, such as holography/interferometry, transport of intensity equation (TIE), phase retrieval (optimization-based approaches), wavefront sensing, and deep-learning-based approaches.

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• mediumreadme#2

  Explicitly state the repo's value for comparing methods in the README introduction

  Why:

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  This collection is particularly useful for understanding and comparing various approaches, including traditional and deep-learning-based methods for phase recovery.

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• mediumtopics#3

  Add 'awesome-list' and 'resources' to the repository topics

  Why:

  CURRENT

  computational-imaging, deep-learning, holography, interferometry, phase-imaging, phase-reconstruction, phase-recovery, phase-retrieval, ptychography, wavefront-sensing

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  computational-imaging, deep-learning, holography, interferometry, phase-imaging, phase-reconstruction, phase-recovery, phase-retrieval, ptychography, wavefront-sensing, awesome-list, resources

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to reconstruct the phase of a light field from its measured intensity data?

  you: not recommended
  AI recommended (in order):

  1. Gerchberg-Saxton Algorithm
  2. Fienup's Hybrid Input-Output (HIO) Algorithm
  3. Error Reduction (ER) Algorithm
  4. Ptychography
  5. ePIE (extended Ptychographical Iterative Engine)
  6. rPIE (reweighted Ptychographical Iterative Engine)
  7. Transport of Intensity Equation (TIE)
  8. Digital Holography
  9. Off-axis Holography
  10. In-line Holography
  11. U-Net
  12. Generative Adversarial Networks (GANs)
  13. Wigner Distribution Deconvolution

  AI recommended 13 alternatives but never named kqwang/phase-recovery. This is the gap to close.

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• CATEGORY QUERY

  Comparing deep learning approaches for computational phase imaging versus traditional methods?

  you: not recommended
  AI recommended (in order):

  1. PyTorch
  2. TensorFlow
  3. JAX
  4. DeepMind's Haiku
  5. Flax
  6. SciPy
  7. OpenCV
  8. MATLAB
  9. NumPy
  10. Pillow

  AI recommended 10 alternatives but never named kqwang/phase-recovery. This is the gap to close.

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• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of kqwang/phase-recovery?

  pass
  ✓AI named kqwang/phase-recovery explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts kqwang/phase-recovery in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named kqwang/phase-recovery explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo kqwang/phase-recovery solve, and who is the primary audience?

  pass
  ✓AI named kqwang/phase-recovery explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?