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Proceedings Paper

Multi-channel compressed sensing optimization based on singular value decomposition
Author(s): Cheng Zhang; Yuanyuan Zhu; Jun Tang; Qianwen Chen; Meiqin Wang
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Paper Abstract

Distributed compressed sensing theory is applied to many practical problems, ECG signal, color imaging, etc. In order to improve the reconstruction accuracy of multi-dimensional signals, this paper applies singular value decomposition to the multi-measure vector problem in DCS, then distributed compressed sensing reconstruction method based on singular value decomposition is proposed. This method can achieve row orthogonality of the measurement matrix and does not affect the design of the reconstruction matrix. Numerical experiments verify the effectiveness of the proposed method, which can significantly improve the reconstruction quality of the signal and the robustness to noise.

Paper Details

Date Published: 14 August 2019
PDF: 4 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794O (14 August 2019); doi: 10.1117/12.2540461
Show Author Affiliations
Cheng Zhang, Anhui Univ. (China)
Yuanyuan Zhu, Anhui Univ. (China)
Jun Tang, Anhui Univ. (China)
Qianwen Chen, Anhui Univ. (China)
Meiqin Wang, 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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