prNet: Data-Driven Phase Retrieval via Stochastic Refinement

• URL: http://arxiv.org/abs/2507.09608v1
• Date: Sun, 13 Jul 2025 12:25:06 GMT
• Title: prNet: Data-Driven Phase Retrieval via Stochastic Refinement
• Abstract summary: We propose a novel framework for phase retrieval that leverages Langevin dynamics to enable efficient posterior sampling.<n>Our method navigates the perception-distortion tradeoff through a combination of sampling, learned denoising, and model-based updates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel framework for phase retrieval that leverages Langevin dynamics to enable efficient posterior sampling, yielding reconstructions that explicitly balance distortion and perceptual quality. Unlike conventional approaches that prioritize pixel-wise accuracy, our method navigates the perception-distortion tradeoff through a principled combination of stochastic sampling, learned denoising, and model-based updates. The framework comprises three variants of increasing complexity, integrating theoretically grounded Langevin inference, adaptive noise schedule learning, parallel reconstruction sampling, and warm-start initialization from classical solvers. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple benchmarks, both in terms of fidelity and perceptual quality.

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