期刊
OPTICA
卷 4, 期 9, 页码 1117-1125出版社
Optica Publishing Group
DOI: 10.1364/OPTICA.4.001117
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资金
- Singapore-MIT Alliance for Research and Technology Centre (SMART)
- Intelligence Advanced Research Projects Activity (IARPA)
- U.S. Department of Energy (DOE) [DE-FG02-97ER25308]
Deep learning has been proven to yield reliably generalizable solutions to numerous classification and decision tasks. Here, we demonstrate for the first time to our knowledge that deep neural networks (DNNs) can be trained to solve end-to-end inverse problems in computational imaging. We experimentally built and tested a lensless imaging system where a DNN was trained to recover phase objects given their propagated intensity diffraction patterns. (C) 2017 Optical Society of America
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