Color computational ghost imaging based on a plug-and-play generalized alternating projection.

Abstract

Computational ghost imaging (CGI), in which an image is retrieved from the known speckle patterns that illuminate the object and the total transmitted intensity, has shown great advances because of its advantages and potential applications at all wavelengths. However, high-quality and less time-consuming imaging has been proven challenging especially in color CGI. In this paper, we will present a new color CGI method that can achieve the reconstruction of high-fidelity images at a relatively low sampling rate (0.0625) by using plug-and-play generalized alternating projection algorithm (PnP-GAP). The spatial distribution and color information of the object are encoded into a one-dimensional light intensity sequence simultaneously by combining randomly distributed speckle patterns and a Bayer color mask as modulation patterns, which is measured by a single-pixel detector. A pre-trained deep denoising network is utilized in the PnP-GAP algorithm to achieve better results. Furthermore, a joint reconstruction and demosaicking method is developed to restore the target color information more realistically. Simulations and optical experiments are performed to verify the feasibility and superiority of our proposed scheme by comparing it with other classical reconstruction algorithms. This new color CGI scheme will enable CGI to obtain information in real scenes more effectively and further promote its practical applications.