Elastic net with adaptive weight for image denoising

Jun Xiao, Rui Zhao, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Sparse models have been widely used in image denoising, and have achieved state-of-the-art performance in past years. Dictionary learning and sparse code estimation are the two key issues for sparse models. When a dictionary is learned, sparse code estimation is equivalent to a general least absolute shrinkage and selection operator (LASSO) problem. However, there are two limitations of LASSO: 1). LASSO gives rise to a biased estimation. 2). LASSO cannot select highly correlated features simultaneously. In recent years, methods for dictionary construction based on the nonlocal self-similarity property and weighted sparse model, relying on noise estimation, have been proposed. These methods can reduce the biased gap of the estimation, and thus achieve promising results for image denoising. In this paper, we propose an elastic net with adaptive weight for image denoising. Our proposed model can achieve nearly unbiased estimation and select highly correlated features. Experimental results show that our proposed method outperforms other state-of-the-art image denoising methods.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2020
EditorsPhooi Yee Lau, Mohammad Shobri
ISBN (Electronic)9781510638358
Publication statusPublished - Jun 2020
EventInternational Workshop on Advanced Imaging Technology, IWAIT 2020 - Yogyakarta, Indonesia
Duration: 5 Jan 20207 Jan 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Workshop on Advanced Imaging Technology, IWAIT 2020


  • Image denoising
  • Sparse coding
  • Weighted sparse model

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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