@inproceedings{1c86653c152f4eea87a298237c7e0e18,
title = "Total variation denoising with overlapping group sparsity",
abstract = "This paper describes an extension to total variation denoising wherein it is assumed the first-order difference function of the unknown signal is not only sparse, but also that large values of the first-order difference function do not generally occur in isolation. This approach is designed to alleviate the staircase artifact often arising in total variation based solutions. A convex cost function is given and an iterative algorithm is derived using majorization-minimization. The algorithm is both fast converging and computationally efficient due to the use of fast solvers for banded systems.",
keywords = "L norm, convex optimization, denoising, filter, group sparsity, sparse signal processing, total variation",
author = "Selesnick, {Ivan W.} and Chen, {Po Yu}",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6638755",
language = "English (US)",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "5696--5700",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}