用生成模型提升相位恢复稳定性，避免噪声下过拟合
PtyGenography: using generative models for regularization of the phase retrieval problem
- 将生成模型作为先验，统一处理经典与生成式反问题
- 新方法在不同噪声水平下均减少生成模型偏差
- 适合需要鲁棒重建的成像与信号恢复场景
在相位恢复等反问题中，解在不同噪声水平下的稳定性对应用至关重要。一种提升稳定性的方法是使用生成模型作为信号先验进行正则化，但会引入重建偏差。本文探讨并比较了经典与生成式反问题求解方法的重建性能。提出一种新的统一重构框架，有效缓解了在不同噪声水平下对生成模型的过拟合问题。
原文摘要 · Abstract (English)
In phase retrieval and similar inverse problems, the stability of solutions across different noise levels is crucial for applications. One approach to promote it is using signal priors in a form of a generative model as a regularization, at the expense of introducing a bias in the reconstruction. In this paper, we explore and compare the reconstruction properties of classical and generative inverse problem formulations. We propose a new unified reconstruction approach that mitigates overfitting to the generative model for varying noise levels.
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