PrNet: Efficient and Robust Phase Retrieval via Stochastic Refinement

Date

2025-01-01

Author

Kaya, Mehmet Onurcan
Öktem, Sevinç Figen

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Phase retrieval is a fundamental inverse problem that arises in many scientific and engineering disciplines, where the goal is to reconstruct a signal from intensity-only measurements. In this work, we develop prNet, a novel phase retrieval framework that stochastically refines initial reconstructions with learned denoising and model-based updates. Our framework combines Langevin dynamics-based posterior sampling, adaptive noise schedule learning, warm-start initialization from classical solvers, and a progressive training strategy inspired by algorithm unrolling. By considering the perception-distortion tradeoff, our method also mitigates the over-smoothing effects commonly observed in prior approaches and enables reconstructions with fine details while minimizing artifacts. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in both efficiency and reconstruction quality.

Subject Keywords

computational imaging, deep learning, diffusion models, inverse problems, Phase retrieval

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105022071991&origin=inward
https://hdl.handle.net/11511/116982

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Department of Electrical and Electronics Engineering, Conference / Seminar