Fast iteratively reweighted least squares for lp regularized image deconvolution and reconstruction

Xu Zhou, Rafael Molina, Fugen Zhou, Aggelos K. Katsaggelos

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

20 Scopus citations

Abstract

Iteratively reweighted least squares (IRLS) is one of the most effective methods to minimize the lp regularized linear inverse problem. Unfortunately, the regularizer is nonsmooth and nonconvex when 0 < p < 1. In spite of its properties and mainly due to its high computation cost, IRLS is not widely used in image deconvolution and reconstruction. In this paper, we first derive the IRLS method from the perspective of majorization minimization and then propose an Alternating Direction Method of Multipliers (ADMM) to solve the reweighted linear equations. Interestingly, the resulting algorithm has a shrinkage operator that pushes each component to zero in a multiplicative fashion. Experimental results on both image deconvolution and reconstruction demonstrate that the proposed method outperforms state-of-the-art algorithms in terms of speed and recovery quality.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1783-1787
Number of pages5
ISBN (Electronic)9781479957514
DOIs
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Keywords

  • Image restoration
  • compressive sensing
  • image reconstruction
  • iteratively reweighted least squares
  • nonconvex nonsmooth regularization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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