Journal
OPTICS EXPRESS
Volume 28, Issue 3, Pages 3717-3731Publisher
OPTICAL SOC AMER
DOI: 10.1364/OE.382319
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Funding
- National Natural Science Foundation of China [61805145, 11774240]
- Leading Talents Program of Guangdong Province [00201505]
- Natural Science Foundation of Guangdong Province [2016A030312010]
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Deep learning has been extensively applied in many optical imaging problems in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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