Deep Plug-and-Play HIO Approach for Phase Retrieval

• URL: http://arxiv.org/abs/2411.18967v2
• Date: Fri, 17 Jan 2025 06:44:38 GMT
• Title: Deep Plug-and-Play HIO Approach for Phase Retrieval
• Authors: Cagatay Isil, Figen S. Oktem,
• Abstract summary: In the phase retrieval problem, the aim is the recovery of an unknown image from intensity-only measurements. Recent learning-based approaches have emerged as powerful alternatives to the analytical methods for several inverse problems. A novel plug-and-play approach that exploits learning-based prior and efficient update steps has been presented.
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• Abstract: 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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