Color computational ghost imaging by deep learning based on simulation data training.

Abstract

We present a new color computational ghost imaging strategy using a sole single-pixel detector and training by simulated dataset, which can eliminate the actual workload of acquiring experimental training datasets and reduce the sampling times for imaging experiments. First, the relative responsibility of the color computational ghost imaging device to different color channels is experimentally detected, and then enough data sets are simulated for training the neural network based on the response value. Because the simulation process is much simpler than the actual experiment, and the training set can be almost unlimited, the trained network model has good generalization. In the experiment with a sampling rate of only 4.1%, the trained neural network model can still recover the image information from the blurry ghost image, correct the color distortion of the image, and get a better reconstruction result. In addition, with the increase in the sampling rate, the details and color characteristics of the reconstruction result become better and better. Feasibility and stability of the proposed method have been verified by the reconstruction results of the trained network model on the color objects of different complexities.