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Piecewise Sparse Recovery via Piecewise Inverse Scale Space Algorithm with Deletion Rule

Piecewise Sparse Recovery via Piecewise Inverse Scale Space Algorithm with Deletion Rule
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Year:    2020

Author:    Yijun Zhong, Chongjun Li

Journal of Computational Mathematics, Vol. 38 (2020), Iss. 2 : pp. 375–394

Abstract

In some applications, there are signals with piecewise structure to be recovered. In this paper, we propose a piecewise_ISS (P_ISS) method which aims to preserve the piecewise sparse structure (or the small-scaled entries) of piecewise signals. In order to avoid selecting redundant false small-scaled elements, we also implement the piecewise_ISS algorithm in parallel and distributed manners equipped with a deletion rule. Numerical experiments indicate that compared with aISS, the P_ISS algorithm is more effective and robust for piecewise sparse recovery.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1810-m2017-0233

Journal of Computational Mathematics, Vol. 38 (2020), Iss. 2 : pp. 375–394

Published online:    2020-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    20

Keywords:    Inverse scale space Piecewise sparse Sparse recovery Small-scaled entries.

Author Details

Yijun Zhong

Chongjun Li

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