期刊
OPTICS AND LASERS IN ENGINEERING
卷 134, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.optlaseng.2020.106183
关键词
Computational ghost imaging; Deep learning; Sub-Nyquist sampling; Image denoising
类别
资金
- National Natural Science Foundation of China [61805048, 61803093, 61672168, 61701123, 51775528, U1801263, U1701262]
- 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]
We propose a deep learning denoising computational ghost imaging (CGI) method to obtain a clear object image with a sub-Nyquist sampling ratio. We develop an end-to-end deep neural network (DDANet) for CGI image reconstruction. DDANet uses a one-dimensional (1-D) bucket signals (BSs) and multiple tunable noise-level maps as input, and outputs a clear image. We train DDANet with simulated BSs and ground-truth pairs, and then retrieve the object image directly from an experimental obtained 1-D BSs. The effectiveness of the proposed method is experimentally investigated. The proposed method has practical applications in image denoising and enhancement of the CGI and single-pixel computational imaging.
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