Nonconvex is attractive: L2/3 regularized thresholding algorithm using multiple sub-dictionaries

Yunyi Li, Yue Hao, Fei Dai, Yue Yin, Shangang Fan, Jie Yang, Guan Gui, Fumiyuki Adachi

研究成果: Conference contribution

抄録

The L2/3-regularization is a typical nonconvex and nonsmooth optimization method, which can obtain more powerful performance than L1 regularization in some applications, such as computational imaging, sparse signal recovery and low-rank matrix completion, etc. This paper proposes an adaptive iteratively-weighted thresholding algorithm for L2/3-regularized problem based on the multiple analysis sub- dictionaries (MD) sparsifying transform strategy, the MD strategy can be employed to further exploit the prior knowledge of estimated signal for sparse recovery. What's more, we propose an adaptive updating scheme for regularization parameter to weight the contribution of each sub-dictionary. Experiments confirm that the proposed method could obtain higher image quality and achieve faster convergence than some corresponding prior work.

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(印刷版)9781538631805
DOI
出版ステータスPublished - 2018 7 27
イベント2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
継続期間: 2018 5 202018 5 24

出版物シリーズ

名前IEEE International Conference on Communications
2018-May
ISSN(印刷版)1550-3607

Other

Other2018 IEEE International Conference on Communications, ICC 2018
国/地域United States
CityKansas City
Period18/5/2018/5/24

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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