Comprehensive comparison of single-pixel imaging methods

Abstract

Single-pixel imaging is a novel computational imaging scheme. It allows one to use a spatially unresolvable photodetector to acquire the spatial information of objects and reconstruct images by a computational means. The core of single-pixel imaging is encoding the spatial information of objects into one-dimensional light signals by using spatial light modulation and subsequently decoding the information to reconstruct the image. Such a novel imaging scheme has a potential of tacking some challenges in conventional imaging, such as imaging with non-visible light, imaging under weak-light conditions, imaging through turbid media, etc. However, the practicality of single-pixel imaging is limited by its imaging quality and efficiency. Thus, how to improve the imaging quality and reduce the imaging time in single-pixel imaging is extensively explored in the field. In this paper, we comprehensively investigate five representative and widely used single-pixel imaging methods – computational ghost imaging, compressive sensing ghost imaging, Hadamard single-pixel imaging, Fourier single-pixel imaging, and binary Fourier single-pixel imaging. We review the principle of these methods and compare the performance of these methods by numerical simulations and experiments. The comparison not only reveals the connections and differences among these methods, but also shows the advantages and disadvantages of their own. This paper also discusses the problems that remain to be solved in single-pixel imaging and prospects for the future development.