用学习先验提升相位恢复效果，兼顾精度与速度。
Deep Plug-and-Play HIO Approach for Phase Retrieval
- 将学习先验融入迭代算法，通过插件式设计实现快速更新
- 在多个数据集上表现优异，对初始值和噪声均具鲁棒性
- 适合需要高精度相位恢复的光学成像场景
相位恢复的目标是从仅有强度的测量数据（如傅里叶强度）中重建未知图像。由于问题本身非线性且病态，求解极具挑战。近年来，基于学习的方法为多种逆问题提供了强大替代方案。本文提出一种新型插件式相位恢复方法，结合学习先验与高效更新步骤，在计算光学传感与成像专题会议上展示出当前最优性能。核心思想是通过插件式正则化将学习先验引入Gerchberg-Saxton类算法。本文给出该方法的数学推导，基于半二次分裂法获得解析更新步骤，并在大规模测试数据集上进行充分仿真对比。结果表明，该方法在图像质量、计算效率及对初始化和噪声的鲁棒性方面均表现出色。
原文摘要 · Abstract (English)
In the phase retrieval problem, the aim is the recovery of an unknown image from intensity-only measurements such as Fourier intensity. Although there are several solution approaches, solving this problem is challenging due to its nonlinear and ill-posed nature. Recently, learning-based approaches have emerged as powerful alternatives to the analytical methods for several inverse problems. In the context of phase retrieval, a novel plug-and-play approach that exploits learning-based prior and efficient update steps has been presented at the Computational Optical Sensing and Imaging topical meeting, with demonstrated state-of-the-art performance. The key idea was to incorporate learning-based prior to the Gerchberg-Saxton type algorithms through plug-and-play regularization. In this paper, we present the mathematical development of the method including the derivation of its analytical update steps based on half-quadratic splitting and comparatively evaluate its performance through extensive simulations on a large test dataset. The results show the effectiveness of the method in terms of both image quality, computational efficiency, and robustness to initialization and noise.
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