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://brendt.wohlberg.net/publications/pdf/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}
}
