@inproceedings{6a41d621391c44ea82728ac506d0769a,
title = "Comparison of deep learning-based compressive imaging from a practitioner's viewpoint",
abstract = "For nearly twenty years, a multitude of Compressive Imaging (CI) techniques have been under development. Modern approaches to CI leverage the capabilities of Deep Learning (DL) tools in order to enhance both the sensing model and the reconstruction algorithm. Unfortunately, most of these DL-based CI methods have been developed by simulating the sensing process while overlooking limitations associated with the optical realization of the optimized sensing model. This article presents an outline of the foremost DL-based CI methods from a practitioner's standpoint. We conduct a comparative analysis of their performances, with a particular emphasis on practical considerations like the feasibility of the sensing matrices and resistance to noise in measurements.",
keywords = "Compressive Imaging, Deep Learning, Neural Networks",
author = "Guy Hanzon and Or Nizhar and Vladislav Kravets and Adrian Stern",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Applications of Machine Learning 2023 ; Conference date: 23-08-2023 Through 24-08-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1117/12.2678022",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zelinski, {Michael E.} and Taha, {Tarek M.} and Narayanan, {Barath Narayanan}",
booktitle = "Applications of Machine Learning 2023",
address = "United States",
}