期刊
OPTICS AND LASERS IN ENGINEERING
卷 110, 期 -, 页码 236-243出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.optlaseng.2018.05.027
关键词
Ghost imaging; De-noising; Principal components analysis; Compandor
类别
资金
- National Basic Research Program of China (973 Program) [2015CB654602]
- 111 Project of China [B14040]
Improving the ghost imaging quality and speed remains a challenging task. Here, we propose an optimization algorithm model to the ghost imaging by employing principal components analysis and companding technique. By choosing appropriate parameters of the principal components and the companding function, the ghost image quality is enhanced. A good agreement between the simulation and the experiment result is obtained. In addition, we demonstrate the method with a complicated sample compared with the other five existing algorithms, indicating its advantages for wide range of applications. At last, a criteria function is firstly proposed and built to optimize the parameters for better reconstruction result without the prior information of the object. This optimization model may offer a promising implementation of de-noising ghost imaging.
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