Quantum-inspired computational imaging
Author(s) -
Yoann Altmann,
Stephen McLaughlin,
Miles J. Padgett,
Vivek K Goyal,
Alfred O. Hero,
Daniele Faccio
Publication year - 2018
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.aat2298
Subject(s) - computer science , peering , artificial intelligence , computer vision , imaging science , obstacle , ghost imaging , integral imaging , detector , lens (geology) , optics , physics , image (mathematics) , telecommunications , the internet , world wide web , political science , law
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.
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