Kang Hanqiu, Wang Yijun, Zhang Ling, Huang Duan
School of Automation, Central South University, Changsha, 410083, China.
School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Sci Rep. 2021 Mar 24;11(1):6776. doi: 10.1038/s41598-021-86275-2.
High-quality reconstruction under a low sampling rate is very important for ghost imaging. How to obtain perfect imaging results from the low sampling rate has become a research hotspot in ghost imaging. In this paper, inspired by matrix optimization in compressed sensing, an optimization scheme of speckle patterns via measurement-driven framework is introduced to improve the reconstruction quality of ghost imaging. According to this framework, the sampling matrix and sparse basis are optimized alternately using the sparse coefficient matrix obtained from the low-dimension pseudo-measurement process and the corresponding solution is obtained analytically, respectively. The optimized sampling matrix is then dealt with non-negative constraint and binary quantization. Compared to the developed optimization schemes of speckle patterns, simulation results show that the proposed scheme can achieve better reconstruction quality with the low sampling rate in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity index (MSSIM). In particular, the lowest sampling rate we use to achieve a good performance is about 6.5%. At this sampling rate, the MSSIM and PSNR of the proposed scheme can reach 0.787 and 17.078 dB, respectively.
在低采样率下进行高质量重建对于鬼成像非常重要。如何从低采样率获得完美的成像结果已成为鬼成像领域的一个研究热点。本文受压缩感知中矩阵优化的启发，引入了一种基于测量驱动框架的散斑图案优化方案，以提高鬼成像的重建质量。根据该框架，利用从低维伪测量过程中获得的稀疏系数矩阵交替优化采样矩阵和稀疏基，并分别解析得到相应的解。然后对优化后的采样矩阵进行非负约束和二值量化。与已有的散斑图案优化方案相比，仿真结果表明，该方案在低采样率下，在峰值信噪比（PSNR）和平均结构相似性指数（MSSIM）方面能够实现更好的重建质量。特别是，我们用于实现良好性能的最低采样率约为6.5%。在此采样率下，该方案的MSSIM和PSNR分别可达0.787和17.078 dB。
