A MULTILEVEL ALGORITHM FOR SIMULTANEOUSLY DENOISING AND DEBLURRING IMAGES

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

21 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1043-1063
Journal / PublicationSIAM Journal on Scientific Computing
Volume32
Issue number2
Online published31 Mar 2010
Publication statusPublished - 2010
Externally publishedYes

Abstract

In this paper, we develop a fast multilevel algorithm for simultaneously denoising and deblurring images under the total variation regularization. Although much effort has been devoted to developing fast algorithms for the numerical solut ion and the denoising problem was satisfactorily solved, fast algorithms for the combined denoising and deblurring model remain to be a challenge. Recently several successful studies of approximating this model and subsequently finding fast algorithms were conducted which have partially solved this problem. The aim of this paper is to generalize a fast multilevel denoising method to solving the minimization model for simultaneously denoising and deblurring. Our new idea is to overcome the complexity issue by a detailed study of the structured matrices that are associated with the blurring operator. A fast algorithm can then be obtained for directly solving the variational model. Supporting numerical experiments on gray scale images are presented.

Research Area(s)

  • Denoising and deblurring, Image restoration, Multilevel methods, Total variation, Uegularization