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arXiv:1710.08343 (cs)
[Submitted on 19 Oct 2017]

Title:Computational ghost imaging using deep learning

Authors:Tomoyoshi Shimobaba, Yutaka Endo, Takashi Nishitsuji, Takayuki Takahashi, Yuki Nagahama, Satoki Hasegawa, Marie Sano, Ryuji Hirayama, Takashi Kakue, Atsushi Shiraki, Tomoyoshi Ito
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics)
Cite as: arXiv:1710.08343 [cs.CV]
  (or arXiv:1710.08343v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1710.08343
Related DOI: https://doi.org/10.1016/j.optcom.2017.12.041

Submission history

From: Tomoyoshi Shimobaba Dr. [view email]
[v1] Thu, 19 Oct 2017 01:54:52 UTC (1,350 KB)
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