  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.
