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Proceedings Paper

Application of joint orthogonal bases in compressive sensing ghost image
Author(s): Xiang Fan; Yi Chen; Zheng-dong Cheng; Zheng-yu Liang; Bin Zhu
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Paper Abstract

Sparse decomposition is one of the core issue of compressive sensing ghost image. At this stage, traditional methods still have the problems of poor sparsity and low reconstruction accuracy, such as discrete fourier transform and discrete cosine transform. In order to solve these problems, joint orthogonal bases transform is proposed to optimize ghost imaging. First, introduce the principle of compressive sensing ghost imaging and point out that sparsity is related to the minimum sample data required for imaging. Then, analyze the development and principle of joint orthogonal bases in detail and find out it can use less nonzero coefficients to reach the same identification effect as other methods. So, joint orthogonal bases transform is able to provide the sparsest representation. Finally, the experimental setup is built in order to verify simulation results. Experimental results indicate that the PSNR of joint orthogonal bases is much higher than traditional methods by using same sample data in compressive sensing ghost image.Therefore, joint orthogonal bases transform can realize better imaging quality under less sample data, which can satisfy the system requirements of convenience and rapid speed in ghost image.

Paper Details

Date Published: 7 November 2016
PDF: 8 pages
Proc. SPIE 10141, Selected Papers of the Chinese Society for Optical Engineering Conferences held July 2016, 101410Q (7 November 2016); doi: 10.1117/12.2253814
Show Author Affiliations
Xiang Fan, Electronic Engineering Institute (China)
Yi Chen, Electronic Engineering Institute (China)
Zheng-dong Cheng, Electronic Engineering Institute (China)
Zheng-yu Liang, Electronic Engineering Institute (China)
Bin Zhu, Electronic Engineering Institute (China)


Published in SPIE Proceedings Vol. 10141:
Selected Papers of the Chinese Society for Optical Engineering Conferences held July 2016
Yueguang Lv; Weimin Bao; Guangjun Zhang, Editor(s)

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