Complexity-regularized image denoising

Juan Liu, Pierre Moulin

Research output: Contribution to journalArticlepeer-review


We study a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional l 2, l 1, and Besov regularization methods. Different complexity measures are considered, in particular those induced by state-of-the-art image coders. We focus on a Gaussian denoising problem and derive a connection between complexity-regularized denoising and operational rate-distortion optimization. This connection suggests the use of efficient algorithms for computing complexity-regularized estimates. Bounds on denoising performance are derived in terms of an index of resolvability that characterizes the compressibility of the true image. Comparisons with state-of-the-art denoising algorithms are given.

Original languageEnglish (US)
Pages (from-to)841-851
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number6
StatePublished - Jun 2001


  • Image compression
  • Image restoration
  • Minimum description length principle
  • Rate-distortion optimization
  • Regularization
  • Wavelets

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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