Visual Computing

@IST Austria

Total Variation on a Tree

TV-l2 denoising. (a) shows the TV-Tree test image, which has been degraded by adding zero-mean Gaussian noise. (b) shows the result of TV-l2 denoising.

Authors

Affiliations

Publication

SIAM Journal on Imaging Sciences (SIIMS), 9(2):605-636, 2016

Abstract

We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems. We treat both the convex and the non-convex case and derive worst case complexities that are equal or better then existing methods. We show applications to total variation based 2D image processing and computer vision problems based on a Lagrangian decomposition approach. The resulting algorithms are very efficient, offer a high degree of parallelism and come along with memory requirements which are only in the order of the number of image pixels.

Citation

@article{kolmogorov2016total,
title={Total variation on a tree},
author={Kolmogorov, Vladimir and Pock, Thomas and Rolinek, Michal},
journal={SIAM Journal on Imaging Sciences},
volume={9},
number={2},
pages={605--636},
year={2016},
publisher={SIAM}
}