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

Zonghao Liu; Bohan Yang; Yifei Zhang; Junfei Shen; Xin Yuan; Mu Ku Chen; Fei Liu; Zihan Geng

doi:10.1038/s41377-025-02021-7

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

Original Articles

- Comprehensive compensation of real-world degradations for robust single-pixel imaging
- Light: Science & Applications Vol. 14, Issue 12, Pages: 3979-3989(2025)
- Affiliations：
  
  1.Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
  2.Pengcheng Laboratory, Shenzhen, China
  3.College of Electronics and Information Engineering, Sichuan University, Chengdu, China
  4.AI Department, School of Engineering, Westlake University, Hangzhou, China
  5.Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
  6.School of Optoelectronic Engineering, Xidian University, Xi’an, China
- Author bio：
  
  Mu Ku Chen (mkchen@cityu.edu.hk)
  Fei Liu (feiliu@xidian.edu.cn)
  Zihan Geng (geng.zihan@sz.tsinghua.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-02021-7
  CLC：
- Received：10 February 2025，
  
  Revised：2025-08-19，
  
  Accepted：20 August 2025，
  
  Published Online：13 October 2025，
  
  Published：31 December 2025
- Accepted：
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Liu, Z. et al. Comprehensive compensation of real-world degradations for robust single-pixel imaging. Light: Science & Applications, 14, 3979-3989 (2025).
DOI：

Liu, Z. et al. Comprehensive compensation of real-world degradations for robust single-pixel imaging. Light: Science & Applications, 14, 3979-3989 (2025). DOI： 10.1038/s41377-025-02021-7.

摘要

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.

关键词

Keywords

references

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