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Cite Details

Paul Rodríguez and Brendt Wohlberg, "An Iteratively Weighted Norm Algorithm for Total Variation Regularization", in Proceedings of the 2006 Asilomar Conference on Signals, Systems, and Computers, (Pacific Grove, CA, USA), doi:10.1109/ACSSC.2006.354879, pp. 892--896, Oct 2006

Abstract

Total Variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. Recently, a number of authors have noted the advantages, including superior performance with certain non-Gaussian noise, of replacing the standard l2 data fidelity term with an l1 norm. We propose a simple but very flexible and computationally efficient method, the Iteratively Reweighted Norm algorithm, for minimizing a generalized TV functional which includes both the l2-TV and and l1-TV problems.

BibTeX Entry

@inproceedings{rodriguez-2006-iteratively,
author = {Paul Rodr\'{i}guez and Brendt Wohlberg},
title = {An Iteratively Weighted Norm Algorithm for Total Variation Regularization},
year = {2006},
month = Oct,
urlpdf = {http://math.lanl.gov/~brendt/Publications/Docs/rodriguez-2006-iteratively.pdf},
booktitle = {Proceedings of the 2006 Asilomar Conference on Signals, Systems, and Computers},
address = {Pacific Grove, CA, USA},
doi = {10.1109/ACSSC.2006.354879},
pages = {892--896}
}