Edgeenhancing Filters with Negative Weights
Abstract
In [DOI:10.1109/ICMEW.2014.6890711], a graphbased denoising is performed by projecting the noisy image to a lower dimensional Krylov subspace of the graph Laplacian, constructed using nonnegative weights determined by distances between image data corresponding to image pixels. We~extend the construction of the graph Laplacian to the case, where some graph weights can be negative. Removing the positivity constraint provides a more accurate inference of a graph model behind the data, and thus can improve quality of filters for graphbased signal processing, e.g., denoising, compared to the standard construction, without affecting the costs.
 Publication:

arXiv eprints
 Pub Date:
 September 2015
 arXiv:
 arXiv:1509.02491
 Bibcode:
 2015arXiv150902491K
 Keywords:

 Computer Science  Computer Vision and Pattern Recognition;
 Computer Science  Information Theory;
 Mathematics  Combinatorics;
 68U10;
 05C85;
 I.4.3;
 I.4.6;
 I.5.4
 EPrint:
 5 pages