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Proceedings Paper

Singular value decomposition compressed ghost imaging based on non-negative constraints
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Paper Abstract

Compressed ghost imaging can effectively enhance the quality of original image from far fewer measurements, but due to the non-negativity of the measurement matrix, the recover quality is thus limited. In this paper, singular value decomposition compressed ghost imaging is proposed; First, the singular value decomposition be used to decompose the measurement matrix, and then the optimized measurement matrix and measurements are used to recover the original image. Numerical experiments verify the superiority of our proposed singular value decomposition compression ghost imaging method.

Paper Details

Date Published: 14 August 2019
PDF: 5 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793P (14 August 2019); doi: 10.1117/12.2540201
Show Author Affiliations
Cheng Zhang, Anhui Univ. (China)
Jun Tang, Anhui Univ. (China)
Meiqin Wang, Anhui Univ. (China)
Qianwen Chen, Anhui Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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