A novel approach of parallel retina-like computational ghost imaging

Jie Cao, Dong Zhou, Fanghua Zhang, Huan Cui, Yingqiang Zhang, Qun Hao*

*此作品的通讯作者

科研成果: 期刊稿件快报同行评审

9 引用 (Scopus)

摘要

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.

源语言英语
文章编号7093
页(从-至)1-13
页数13
期刊Sensors
20
24
DOI
出版状态已出版 - 2 12月 2020

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Cao, J., Zhou, D., Zhang, F., Cui, H., Zhang, Y., & Hao, Q. (2020). A novel approach of parallel retina-like computational ghost imaging. Sensors, 20(24), 1-13. 文章 7093. https://doi.org/10.3390/s20247093