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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538631805
Publication statusPublished - 2018 Jul 27
Event2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
Duration: 2018 May 202018 May 24

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Other2018 IEEE International Conference on Communications, ICC 2018
Country/TerritoryUnited States
CityKansas City


  • Image restoration
  • Iteratively thresholding
  • L regularization
  • Multiple subdictionaries sparsifying transform
  • Nonconvex

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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