An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals

Author

Wohlberg, Brendt ; Rodríguez, Paul

Author_Institution

Los Alamos Nat. Lab., Los Alamos

Volume

14

Issue

12

fYear

2007

Firstpage

948

Lastpage

951

Abstract

Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard lscr²data fidelity term with an lscr¹ norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the lscr² -TV and lscr¹ -TV problems as special cases. This method offers competitive computational performance for lscr² -TV and is comparable to or faster than any other lscr¹ -TV algorithms of which we are aware.

Keywords

deconvolution; image denoising; image restoration; inverse problems; image deconvolution; image denoising; image restoration problem; inverse problem; iteratively reweighted norm algorithm; lscr¹ norm; lscr²data fidelity term; total variation functional minimization; Deconvolution; Forward contracts; Image restoration; Inverse problems; Iterative algorithms; Minimization methods; Noise reduction; Sparse matrices; TV; Vectors; Image restoration; inverse problem; regularization; total variation;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2007.906221

Filename

4380459