Computational ghost imaging using deep learning
This addresses image quality issues for researchers in single-pixel imaging, but it is incremental as it applies an existing deep learning method to a known noise problem.
The study tackled the problem of noise-reduced image quality in computational ghost imaging by applying a deep neural network to learn features from noise-contaminated images, resulting in the network predicting low-noise images from new inputs.
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.