A Closer Look at Reference Learning for Fourier Phase Retrieval

• URL: http://arxiv.org/abs/2110.13688v1
• Date: Tue, 26 Oct 2021 13:25:36 GMT
• Title: A Closer Look at Reference Learning for Fourier Phase Retrieval
• Authors: Tobias Uelwer, Nick Rucks, Stefan Harmeling
• Abstract summary: 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 propose a simple and efficient to construct reference images that, in some cases, yields reconstructions of comparable quality as approaches that learn references.
• Score: 0.966840768820136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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