Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Closer Look at Reference Learning for Fourier Phase Retrieval   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.

36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an