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Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm |
| Haipeng Zhang(张海鹏)1,2,3, Ke Li(李可)2, Changzhe Zhao(赵昌哲)1,2,3, Jie Tang(汤杰), and Tiqiao Xiao(肖体乔)1,2,3,† |
1 Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China;
2 Shanghai Synchrotron Radiation Facility/Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China;
3 University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Towards efficient implementation of x-ray ghost imaging (XGI), efficient data acquisition and fast image reconstruction together with high image quality are preferred. In view of radiation dose resulted from the incident x-rays, fewer measurements with sufficient signal-to-noise ratio (SNR) are always anticipated. Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously. In this paper, a method based on a modified compressive sensing algorithm with conjugate gradient descent method (CGDGI) is developed to solve the problems encountered in available XGI methods. Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI. The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.
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Received: 22 September 2021
Revised: 02 January 2022
Accepted manuscript online: 07 January 2022
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PACS:
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42.30.Va
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(Image forming and processing)
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42.30.Wb
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(Image reconstruction; tomography)
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| Fund: Project supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0206004,2017YFA0206002, 2018YFC0206002, and 2017YFA0403801) and National Natural Science Foundation of China (Grant No. 81430087). |
Corresponding Authors:
Tiqiao Xiao
E-mail: xiaotiqiao@zjlab.org.cn
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Cite this article:
Haipeng Zhang(张海鹏), Ke Li(李可), Changzhe Zhao(赵昌哲), Jie Tang(汤杰), and Tiqiao Xiao(肖体乔) Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm 2022 Chin. Phys. B 31 064202
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