Comparison of deep learning-based compressive imaging from a practitioner's viewpoint

Guy Hanzon; Or Nizhar; Vladislav Kravets; Adrian Stern

doi:10.1117/12.2678022

4 October 2023 Comparison of deep learning-based compressive imaging from a practitioner's viewpoint

Guy Hanzon, Or Nizhar, Vladislav Kravets, Adrian Stern

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750I (2023) https://doi.org/10.1117/12.2678022
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

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.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guy Hanzon, Or Nizhar, Vladislav Kravets, and Adrian Stern "Comparison of deep learning-based compressive imaging from a practitioner's viewpoint", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750I (4 October 2023); https://doi.org/10.1117/12.2678022

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