Multi-channel compressed sensing optimization based on singular value decomposition

Cheng Zhang; Yuanyuan Zhu; Jun Tang; Qianwen Chen; Meiqin Wang

doi:10.1117/12.2540461

14 August 2019 Multi-channel compressed sensing optimization based on singular value decomposition

Cheng Zhang, Yuanyuan Zhu, Jun Tang, Qianwen Chen, Meiqin Wang

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111794O (2019) https://doi.org/10.1117/12.2540461
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Cheng Zhang, Yuanyuan Zhu, Jun Tang, Qianwen Chen, and Meiqin Wang "Multi-channel compressed sensing optimization based on singular value decomposition", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794O (14 August 2019); https://doi.org/10.1117/12.2540461

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