Computational ghost imaging enhanced by degradation models for under-sampling

Computational ghost imaging (CGI) allows two-dimensional (2D) imaging by using spatial light modulators and bucket detectors. However, most CGI methods attempt to obtain 2D images through measurements with a single sampling ratio. Here, we propose a CGI method enhanced by degradation models for under-sampling, which can be reflected by results from measurements with different sampling ratios. We utilize results from low-sampling-ratio measurements and normal-sampling-ratio measurements to train the neural network for the degradation model, which is fitted through self-supervised learning. We obtain final results by importing normal-sampling-ratio results into the neural network with optimal parameters. We experimentally demonstrate improved results from the CGI method using degradation models for under-sampling. Our proposed method would promote the development of CGI in many applications.