Computational ghost imaging using deep learning

Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three-dimensional images with a single or a few bucket detect...

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Bibliographic Details
Published in:Optics communications 2018-04, Vol.413, p.147-151
Main Authors: Shimobaba, Tomoyoshi, Endo, Yutaka, Nishitsuji, Takashi, Takahashi, Takayuki, Nagahama, Yuki, Hasegawa, Satoki, Sano, Marie, Hirayama, Ryuji, Kakue, Takashi, Shiraki, Atsushi, Ito, Tomoyoshi
Format: Article
Language:English
Subjects:
Computational ghost imaging
Deep learning
Ghost imaging
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Summary:Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three-dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.
ISSN:0030-4018
1873-0310
DOI:10.1016/j.optcom.2017.12.041