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 detec...

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Bibliographic Details
Published in:arXiv.org 2017-10
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:
Computation
Deep learning
Image quality
Image reconstruction
Luminous intensity
Neural networks
Noise
Noise prediction
Online Access:Get full text
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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:2331-8422
DOI:10.48550/arxiv.1710.08343