Comprehensive compensation of real-world degradations for robust single-pixel imaging.

Abstract

Single-pixel imaging (SPI) faces significant challenges in reconstructing high-quality images under complex real-world degradation conditions. This paper presents an innovative degradation model for the physical processes in SPI, providing the first comprehensive and quantitative analysis of various SPI noise sources encountered in real-world applications. Especially, pattern-dependent global noise propagation and object jitter modelling methods for SPI are proposed. Subsequently, a deep-blind neural network is developed to remove the necessity of obtaining parameters of all the degradation factors in real-world image compensation. Our method can operate without degradation parameters and significantly improve the resolution and fidelity of SPI image reconstruction. The deep-blind network training is guided by the proposed comprehensive SPI degradation model that describes real-world SPI impairments, enabling the network to generalize across a wide range of degradation combinations. The experiment validates its advanced performance in real-world SPI imaging at ultra-low sampling rates. The proposed method holds great potential for applications in remote sensing, biomedical imaging, and privacy-preserving surveillance.