Color computational ghost imaging based on a generative adversarial network.

Abstract

A novel, to the best of our knowledge, color computational ghost imaging scheme is presented for the reconstruction of a color object image, which greatly simplifies the experimental setup and shortens the acquisition time. Compared to conventional schemes, it only adopts one digital light projector to project color speckles and one single-pixel detector to receive the light intensity, instead of utilizing three monochromatic paths separately and synthesizing the three branch results. Severe noise and color distortion, which are common in ghost imaging, can be removed by the utilization of a generative adversarial network, because it has advantages in restoring the image's texture details and generating the image's match to a human's subjective feelings over other generative models in deep learning. The final results can perform consistently better visual quality with more realistic and natural textures, even at the low sampling rate of 0.05.