Abstract Compressed sensing (CS) can recover a signal from a small number of observed transforms of that signal. It mainly consists of two complimentary elements including compressed sampling and computational image reconstruction. In this work, we have developed a ghost imaging system and proposed a primal dual interior point compressed sensing algorithm. We also demonstrated the performance of our imaging system and CS algorithm with simulations and experiment compared to conventional approaches. The discrete samples signal can be reconstructed by our CS algorithm. Experimental results show that the proposed compressed imaging method outperforms the conventional CS approaches in both computational time and reconstruction accuracy.

中文翻译：

---

基于原始对偶内点算法的图像重建快速压缩感知分析

摘要 压缩感知 (CS) 可以从该信号的少量观察变换中恢复该信号。它主要由压缩采样和计算图像重建两个互补元素组成。在这项工作中，我们开发了一个重影成像系统，并提出了一种原始对偶内点压缩感知算法。与传统方法相比，我们还通过模拟和实验证明了我们的成像系统和 CS 算法的性能。离散样本信号可以通过我们的 CS 算法重建。实验结果表明，所提出的压缩成像方法在计算时间和重建精度方面都优于传统的 CS 方法。