Accelerated graph-based nonlinear denoising filters
Computer Vision and Pattern Recognition
2017-01-09 v2 Numerical Analysis
Abstract
Denoising filters, such as bilateral, guided, and total variation filters, applied to images on general graphs may require repeated application if noise is not small enough. We formulate two acceleration techniques of the resulted iterations: conjugate gradient method and Nesterov's acceleration. We numerically show efficiency of the accelerated nonlinear filters for image denoising and demonstrate 2-12 times speed-up, i.e., the acceleration techniques reduce the number of iterations required to reach a given peak signal-to-noise ratio (PSNR) by the above indicated factor of 2-12.
Keywords
Cite
@article{arxiv.1512.00389,
title = {Accelerated graph-based nonlinear denoising filters},
author = {Andrew Knyazev and Alexander Malyshev},
journal= {arXiv preprint arXiv:1512.00389},
year = {2017}
}
Comments
10 pages, 6 figures, to appear in Procedia Computer Science, vol.80, 2016, International Conference on Computational Science, San Diego, CA, USA, June 6-8, 2016