An iterative regularized mixed-norm image restoration algorithm

Min Cheol Hong*, Tania Stathaki, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


This paper introduces a regularized mixed-norm image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals, and a function of the previous two functionals and the smoothing functionals is utilized for determining the regularization parameter. The two parameters are chosen in such a way that the proposed functional is convex, so that a local minimizer becomes a global minimizer. The novelty of the proposed algorithm is that no knowledge of the noise distribution is required, and the relative contribution of the LMS, the LMF and the smoothing functionals is adjusted based on the partially restored image.

Original languageEnglish (US)
Pages (from-to)603-614
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue number2
StatePublished - 1998
EventVisual Communications and Image Processing '98 - San Jose, CA, United States
Duration: Jan 28 1998Jan 30 1998


  • Kurtosis
  • Mixed-norms
  • Regularized image restoration
  • Smoothing functional

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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