PtyGenography: using generative models for regularization of the phase retrieval problem

• URL: http://arxiv.org/abs/2502.01338v1
• Date: Mon, 03 Feb 2025 13:26:55 GMT
• Title: PtyGenography: using generative models for regularization of the phase retrieval problem
• Authors: Selin Aslan, Tristan van Leeuwen, Allard Mosk, Palina Salanevich,
• Abstract summary: We compare the reconstruction properties of classical and generative inverse problem formulations.<n>We propose a new unified reconstruction approach that mitigates overfitting to the generative model for varying noise levels.
• Score: 0.562479170374811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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