Li Xianye, Qi Nan, Jiang Shan, Wang Yurong, Li Xun, Sun Baoqing
Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China.
School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
Sensors (Basel). 2020 Sep 18;20(18):5341. doi: 10.3390/s20185341.
Compressive single-pixel imaging (CSPI) is a novel imaging scheme that retrieves images with nonpixelated detection. It has been studied intensively for its minimum requirement of detector resolution and capacity to reconstruct image with underdetermined acquisition. In practice, CSPI is inevitably involved with noise. It is thus essential to understand how noise affects its imaging process, and more importantly, to develop effective strategies for noise compression. In this work, two ypes of noise classified as multiplicative and additive noises are discussed. A normalized compressive reconstruction scheme is firstly proposed to counteract multiplicative noise. For additive noise, two types of compressive algorithms are studied. We find that pseudo-inverse operation could render worse reconstructions with more samplings in compressive sensing. This problem is then solved by introducing zero-mean inverse measurement matrix. Both experiment and simulation results show that our proposed algorithms significantly surpass traditional methods. Our study is believed to be helpful in not only CSPI but also other denoising works when compressive sensing is applied.
压缩单像素成像（CSPI）是一种新型成像方案，它通过非像素化检测来恢复图像。由于其对探测器分辨率的最低要求以及在欠定采集情况下重建图像的能力，该技术已得到深入研究。在实际应用中，CSPI不可避免地会受到噪声影响。因此，了解噪声如何影响其成像过程至关重要，更重要的是，要开发有效的噪声压缩策略。在这项工作中，讨论了两种类型的噪声，即乘性噪声和加性噪声。首先提出了一种归一化压缩重建方案来抵消乘性噪声。对于加性噪声，研究了两种类型的压缩算法。我们发现伪逆运算在压缩感知中进行更多采样时会导致更差的重建结果。然后通过引入零均值逆测量矩阵解决了这个问题。实验和仿真结果均表明，我们提出的算法明显优于传统方法。我们相信，我们的研究不仅对CSPI有帮助，而且在应用压缩感知时对其他去噪工作也有帮助。
