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Volume 38, Issue 2
Piecewise Sparse Recovery via Piecewise Inverse Scale Space Algorithm with Deletion Rule

Yijun Zhong & Chongjun Li

DOI: 10.4208/jcm.1810-m2017-0233

J. Comp. Math., 38 (2020), pp. 375-394.

Published online: 2020-02

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  • 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.

  • Keywords

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

  • AMS Subject Headings

90C25, 94A12

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

zhongyijun@mail.dlut.edu.cn (Yijun Zhong)

chongjun@dlut.edu.cn (Chongjun Li)

  • BibTex
  • RIS
  • TXT
@Article{JCM-38-375, author = {Zhong , Yijun and Li , Chongjun}, title = {Piecewise Sparse Recovery via Piecewise Inverse Scale Space Algorithm with Deletion Rule}, journal = {Journal of Computational Mathematics}, year = {2020}, volume = {38}, number = {2}, pages = {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.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.1810-m2017-0233}, url = {http://global-sci.org/intro/article_detail/jcm/14522.html} }
TY - JOUR T1 - Piecewise Sparse Recovery via Piecewise Inverse Scale Space Algorithm with Deletion Rule AU - Zhong , Yijun AU - Li , Chongjun JO - Journal of Computational Mathematics VL - 2 SP - 375 EP - 394 PY - 2020 DA - 2020/02 SN - 38 DO - http://doi.org/10.4208/jcm.1810-m2017-0233 UR - https://global-sci.org/intro/article_detail/jcm/14522.html KW - Inverse scale space, Piecewise sparse, Sparse recovery, Small-scaled entries. AB -

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.

Zhong , Yijun and Li , Chongjun. (2020). Piecewise Sparse Recovery via Piecewise Inverse Scale Space Algorithm with Deletion Rule. Journal of Computational Mathematics. 38 (2). 375-394. doi:10.4208/jcm.1810-m2017-0233
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