Journal
OPTICS EXPRESS
Volume 28, Issue 3, Pages 3846-3853Publisher
OPTICAL SOC AMER
DOI: 10.1364/OE.386976
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Funding
- National Natural Science Foundation of China [51775528, 61672168, 61701123, 61803093, 61805048]
- China Scholarship Council (CSC) [201808440010]
- Guangdong Provincial Key Laboratory of Cyber-Physical System [2016B030301008]
- Natural Science Foundation of Guangdong Province [2018A030310599]
- Application Technologies R&D Program of Guangdong Province [2015B090922013]
- Key Area R&D Plan Program of Guangdong Province [2016B090918017, CXZJHZ201730]
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We propose a deep learning computational ghost imaging (CGI) scheme to achieve sub-Nyquist and high-quality image reconstruction. Unlike the second-order-correlation CGI and compressive-sensing CGI, which use lots of illumination patterns and a one-dimensional (1-D) light intensity sequence (LIS) for image reconstruction, a deep neural network (DAttNet) is proposed to restore the target image only using the 1-D LIS. The DAttNet is trained with simulation data and retrieves the target image from experimental data. The experimental results indicate that the proposed scheme can provide high-quality images with a sub-Nyquist sampling ratio and performs better than the conventional and compressive-sensing CGI methods in sub-Nyquist sampling ratio conditions (e.g., 5.45%). The proposed scheme has potential practical applications in underwater, real-time and dynamic CGI. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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