Items available from this webpage
- Complete TV package to reproduce all the results of the paper
- proxTV: fast Total-Variation toolbox (C++, Matlab)
- Preprint / longer version of paper
- C++ implementation (using the proxTV toolbox) of the ADMM based L1-trend filtering method
References
-
Modular proximal minimization for total variation regularization
by A. Barbero and S. Sra
[.bib]; [Preprint: 1411.0589; Hyperlinked PDF:] (Oct. 2014; Nov. 2013);@Article{barSra14, author = {\'A. Barbero and S. Sra}, title = {{Modular proximal optimization for multidimensional total-variation regularization}}, journal = {arXiv:1411.0589, year = {2014}, note = {\it Submitted} } -
Fast Newton-type Methods for Total-Variation with Applications
by Á. J. Barbero, S. Sra
International Conference on Machine Learning (ICML) June, 2011;
[.bib]; [Paper: .pdf]@InProceedings{barSra11, author = {\'A. J. Barbero and S. Sra}, title = {{Fast Newton-type Methods for Total-Variation with Applications}}, booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, year = 2011, month = {Jan.}, } -
Fast algorithms for total-variation based optimization
by A. J. Barbero and S. Sra
MPI Technical Report #194. Aug. 2010;
[.bib]; [Paper: .pdf ]@TechReport{tr.alvaro, author = {\'A. J. Barbero and {\mybf S. Sra}}, title = {Fast algorithms for total-variation based optimization}, institution = {Max-Planck Institute for Intelligent Systems}, year = {2010}, number = {194}, month = {Aug.}, }