From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms

Ling Shao, Ruomei Yan, Xuelong Li, Yan Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

293 Citations (Scopus)


Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.
Original languageEnglish
Article number6587769
Pages (from-to)1001-1013
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number7
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Adaptive filters
  • dictionary learning
  • evaluation
  • image denoising
  • sparse coding
  • spatial domain
  • survey
  • transform domain

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this