近日，清华大学耿子涵团队报道了全面补偿真实世界的退化，实现稳健的单像素成像。相关论文于2025年10月13日发表在《光：科学与应用》杂志上。

单像素成像（SPI）在复杂的现实退化条件下重建高质量图像面临着重大挑战。

研究组提出了一种创新的SPI物理过程退化模型，首次对实际应用中遇到的各种SPI噪声强度进行了全面和定量的分析。特别提出了基于模式的SPI全局噪声传播和目标抖动建模方法。随后，开发了一种深度盲神经网络，消除了在实际图像补偿中需要获取所有退化因素参数的必要性。该方法无退化参数，可显著提高SPI图像重建的分辨率和保真度。

深度盲网络训练由所提出的综合SPI退化模型指导，该模型描述了现实世界的SPI缺陷，使网络能够在广泛的退化组合中进行推广。实验验证了其在超低采样率下的实际SPI成像中的先进性能。该方法在遥感、生物医学成像和隐私保护监控等领域具有很大的应用潜力。

附：英文原文

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

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

Issue&Volume: 2025-10-13

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.

DOI: 10.1038/s41377-025-02021-7

Source: https://www.nature.com/articles/s41377-025-02021-7