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

Selin Aslan; Tristan van Leeuwen; Allard Mosk; Palina Salanevich

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arXiv · 2502.01338

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

Selin Aslan·Tristan van Leeuwen·Allard Mosk·Palina Salanevich

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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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Selin Aslan, Tristan van Leeuwen, Allard Mosk, Palina Salanevich. 2025-02-03. PtyGenography: using generative models for regularization of the phase retrieval problem. https://arxiv.org/abs/2502.01338

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