Computational ghost imaging using deep learning
Computer Vision and Pattern Recognition
2018-01-17 v1 Optics
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.
Cite
@article{arxiv.1710.08343,
title = {Computational ghost imaging using deep learning},
author = {Tomoyoshi Shimobaba and Yutaka Endo and Takashi Nishitsuji and Takayuki Takahashi and Yuki Nagahama and Satoki Hasegawa and Marie Sano and Ryuji Hirayama and Takashi Kakue and Atsushi Shiraki and Tomoyoshi Ito},
journal= {arXiv preprint arXiv:1710.08343},
year = {2018}
}