School of Optics and Photonics, Beijing Institute of Technology, Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing 100081, China.
Sensors (Basel). 2020 Dec 11;20(24):7093. doi: 10.3390/s20247093.
Computational ghost imaging (CGI), with the advantages of wide spectrum, low cost, and robustness to light scattering, has been widely used in many applications. The key issue is long time correlations for acceptable imaging quality. To overcome the issue, we propose parallel retina-like computational ghost imaging (PRGI) method to improve the performance of CGI. In the PRGI scheme, sampling and reconstruction are carried out by using the patterns which are divided into blocks from designed retina-like patterns. Then, the reconstructed image of each block is stitched into the entire image corresponding to the object. The simulations demonstrate that the proposed PRGI method can obtain a sharper image while greatly reducing the time cost than CGI based on compressive sensing (CSGI), parallel architecture (PGI), and retina-like structure (RGI), thereby improving the performance of CGI. The proposed method with reasonable structure design and variable selection may lead to improve performance for similar imaging methods and provide a novel technique for real-time imaging applications.
计算鬼成像（CGI）具有光谱宽、成本低、对光散射鲁棒等优点，已广泛应用于许多领域。关键问题是为了获得可接受的成像质量需要长时间的相关性。为了解决这个问题，我们提出了并行视网膜计算鬼成像（PRGI）方法来提高 CGI 的性能。在 PRGI 方案中，通过使用从设计的视网膜样图案中划分成块的图案来进行采样和重建。然后，将每个块的重建图像拼接成与物体对应的整个图像。模拟结果表明，与基于压缩感知（CSGI）、并行架构（PGI）和视网膜样结构（RGI）的 CGI 相比，所提出的 PRGI 方法可以在大大降低时间成本的同时获得更清晰的图像，从而提高了 CGI 的性能。所提出的方法具有合理的结构设计和变量选择，可能会提高类似成像方法的性能，并为实时成像应用提供一种新的技术。
