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Multitask classification and reconstruction using extended Turbo approximate message passing

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Abstract

Approximate message passing (AMP) is a known compressive sensing (CS) algorithm, owing to it being computationally efficient, and having high performance and a deterministic state evolution (SE) trajectory. Turbo generalized AMP (Turbo-GAMP) was proposed based on AMP, and it was extended to multitask CS with multiple measurement vectors (MMVs). The resulting Turbo-GAMP-MMV can reconstruct multiple structured-sparse signals when they are well correlated. This paper considers the case where the CS tasks belong to various groups and signals from different groups may have weak correlation. We explore the SE property to enhance Turbo-GAMP-MMV for weakly correlated signals. The developed methods first conduct task classification via dividing CS tasks into groups and then reconstruct the original signals from each group jointly. Experiments using synthetic signals and grey images show that the new algorithms outperform several state-of-art benchmark techniques.

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Authors and Affiliations

  1. Unit 61541, Beijing, 100094, People’s Republic of China

    Ying-Gui Wang, Ze-Ying Tang & Yong Gao

  2. School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi, 214122, Jiangsu, People’s Republic of China

    Le Yang

Authors
  1. Ying-Gui Wang
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  2. Le Yang
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  3. Ze-Ying Tang
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  4. Yong Gao
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Corresponding author

Correspondence to Le Yang.

Additional information

This work was supported by the Natural Science Foundation of China (Nos. 61304264 and 61305017).

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Wang, YG., Yang, L., Tang, ZY. et al. Multitask classification and reconstruction using extended Turbo approximate message passing. SIViP 11, 219–226 (2017). https://doi.org/10.1007/s11760-016-0922-5

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  • DOI: https://doi.org/10.1007/s11760-016-0922-5

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