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Time-Varying Graph Signal Recovery Based on Low-Rank and Piecewise-Differential Smoothness.

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Title: Time-Varying Graph Signal Recovery Based on Low-Rank and Piecewise-Differential Smoothness.
Authors: Liu, Jinling1,2,3 (AUTHOR) liujl@guet.edu.cn, Wang, Junyi2 (AUTHOR) wangjy@guet.edu.cn, Li, Guozhi1,3 (AUTHOR) liguozhi@guet.edu.cn, Lan, Chaowang1,3 (AUTHOR) chaowanglan@guet.edu.cn, Liu, Zhenbing1,3,4 (AUTHOR) zbliu@guet.edu.cn
Source: Circuits, Systems & Signal Processing. Jul2026, Vol. 45 Issue 7, p5546-5572. 27p.
Subjects: Spatiotemporal processes, Difference operators, Smoothness of functions, Signal processing, Signal reconstruction
Abstract: Time-varying piecewise smooth data recovery problem exists extensively in computer vision, image processing, environment monitoring, etc. In recent years, the emerging field of graph signal processing (GSP) 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 jointly exploiting the graph difference operator and the time difference operator to capture the spatio-temporal correlation of the data, we obtain a method based on low-rank and piecewise-differential smoothness (LRPDS). The proposed method achieves high recovery accuracy by jointly exploiting low rank property, the piece- wise smoothness and differential smoothness of graph signals. Numerical results on three real-world datasets demonstrate that our scheme has better reconstruction performance compared with existing model-based matrix completion approaches. [ABSTRACT FROM AUTHOR]
Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 194867702
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  Data: Time-Varying Graph Signal Recovery Based on Low-Rank and Piecewise-Differential Smoothness.
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  Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. Jul2026, Vol. 45 Issue 7, p5546-5572. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Difference+operators%22">Difference operators</searchLink><br /><searchLink fieldCode="DE" term="%22Smoothness+of+functions%22">Smoothness of functions</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink>
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  Data: Time-varying piecewise smooth data recovery problem exists extensively in computer vision, image processing, environment monitoring, etc. In recent years, the emerging field of graph signal processing (GSP) 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 jointly exploiting the graph difference operator and the time difference operator to capture the spatio-temporal correlation of the data, we obtain a method based on low-rank and piecewise-differential smoothness (LRPDS). The proposed method achieves high recovery accuracy by jointly exploiting low rank property, the piece- wise smoothness and differential smoothness of graph signals. Numerical results on three real-world datasets demonstrate that our scheme has better reconstruction performance compared with existing model-based matrix completion approaches. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00034-025-03398-x
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        Text: English
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        Type: general
      – SubjectFull: Difference operators
        Type: general
      – SubjectFull: Smoothness of functions
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      – SubjectFull: Signal processing
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      – SubjectFull: Signal reconstruction
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      – TitleFull: Time-Varying Graph Signal Recovery Based on Low-Rank and Piecewise-Differential Smoothness.
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            NameFull: Liu, Jinling
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            NameFull: Li, Guozhi
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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