Title
An iteratively reweighted norm algorithm for minimization of total variation functionals
Date Issued
01 December 2007
Access level
metadata only access
Resource Type
journal article
Author(s)
Wohlberg B.
Los Alamos National Laboratory
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 ℓ2 data fidelity term with an ℓ1 norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the ℓ2-TV ℓ1-TV and ℓ2-TV problems as special cases. This method offers competitive computational performance for ℓ2-TV and is comparable to or faster than any other ℓ1-TV algorithms of which we are aware. © 2007 IEEE.
Start page
948
End page
951
Volume
14
Issue
12
Language
English
OCDE Knowledge area
Otras ingenierías y tecnologías
Subjects
Scopus EID
2-s2.0-36749031147
PubMed ID
Source
IEEE Signal Processing Letters
ISSN of the container
10709908
Sponsor(s)
Manuscript received April 3, 2007; revised July 13, 2007. This work was carried out under the auspices of the National Nuclear Security Administration of the U.S. Department of Energy at Los Alamos National Laboratory under Contract No. DE-AC52-06NA25396 and was supported in part by the NNSA’s Laboratory Directed Research and Development Program. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Alfred Mertins.
Sources of information:
Directorio de Producción Científica
Scopus