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arXiv · 2110.13688

A Closer Look at Reference Learning for Fourier Phase Retrieval

Tobias Uelwer·Nick Rucks·Stefan Harmeling
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Abstract

Reconstructing images from their Fourier magnitude measurements is a problem that often arises in different research areas. This process is also referred to as phase retrieval. In this work, we consider a modified version of the phase retrieval problem, which allows for a reference image to be added onto the image before the Fourier magnitudes are measured. We analyze an unrolled Gerchberg-Saxton (GS) algorithm that can be used to learn a good reference image from a dataset. Furthermore, we take a closer look at the learned reference images and propose a simple and efficient heuristic to construct reference images that, in some cases, yields reconstructions of comparable quality as approaches that learn references. Our code is available at https://github.com/tuelwer/reference-learning.

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Tobias Uelwer, Nick Rucks, Stefan Harmeling. 2021-10-26. A Closer Look at Reference Learning for Fourier Phase Retrieval. https://arxiv.org/abs/2110.13688

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