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Time-varying signal recovery based on low rank and graph-time smoothness

Time-varying data recovery problem exists extensively in computer vision, image processing, environment monitoring, etc. In recent years, the emerging field of graph signal processing provides a new way to solve this problem, deriving the graph signal matrix completion (GSMC) which incorporates the...

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Bibliographic Details
Published in:Digital signal processing 2023-03, Vol.133, p.103821, Article 103821
Main Authors: Liu, Jinling, Lin, Jiming, Qiu, Hongbing, Wang, Junyi, Nong, Liping
Format: Article
Language:English
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Summary:Time-varying data recovery problem exists extensively in computer vision, image processing, environment monitoring, etc. In recent years, the emerging field of graph signal processing provides a new way to solve this problem, deriving the graph signal matrix completion (GSMC) which incorporates the correlation among data entries. The model-based methods of GSMC are more interpretable, but their reconstruction quality is still not satisfactory, especially when observations are sparse. In this paper, we propose a new matrix completion method to solve the time-varying data recovery problem. By adopting the time series analysis method to capture the evolution of data in the time dimension, we obtain a method based on Low Rank and Graph-Time Smoothness (LRGTS). The proposed method has high recovery accuracy by using the second-order information associated with the problem. Numerical results on three real-world datasets demonstrate that our scheme has better reconstruction performance when known entries are sparse, compared with existing matrix completion approaches. •A graph signal matrix completion method utilizing the second-order smoothness is proposed.•The method shows superior recovery performance.•Simulation results on real-world datasets verify the effectiveness of the method.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2022.103821