College of Physics and Optoelectronics, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China.
Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, and Shanxi Province, Taiyuan University of Technology, No. 79 West Main Street, Taiyuan 030024, China.
Sensors (Basel). 2023 May 11;23(10):4678. doi: 10.3390/s23104678.
Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot of pixels in traditional imaging techniques to realize two-dimensional or even multi-dimensional imaging. For SPI using compressed sensing, the target to be imaged is illuminated by a series of patterns with spatial resolution, and then the reflected or transmitted intensity is compressively sampled by the single-pixel detector to reconstruct the target image while breaking the limitation of the Nyquist sampling theorem. Recently, in the area of signal processing using compressed sensing, many measurement matrices as well as reconstruction algorithms have been proposed. It is necessary to explore the application of these methods in SPI. Therefore, this paper reviews the concept of compressive sensing SPI and summarizes the main measurement matrices and reconstruction algorithms in compressive sensing. Further, the performance of their applications in SPI through simulations and experiments is explored in detail, and then their advantages and disadvantages are summarized. Finally, the prospect of compressive sensing with SPI is discussed.
单像素成像（SPI）使用单像素探测器代替传统成像技术中的具有大量像素的探测器阵列，以实现二维甚至多维成像。对于使用压缩感知的 SPI，目标被空间分辨率的一系列图案照明，然后通过单像素探测器对反射或传输强度进行压缩采样，在突破奈奎斯特采样定理的限制的同时重建目标图像。最近，在使用压缩感知的信号处理领域，已经提出了许多测量矩阵以及重构算法。有必要探索这些方法在 SPI 中的应用。因此，本文回顾了压缩感知 SPI 的概念，并总结了压缩感知中的主要测量矩阵和重构算法。此外，还通过仿真和实验详细探讨了它们在 SPI 中的应用性能，然后总结了它们的优缺点。最后，讨论了 SPI 与压缩感知的前景。
