BibTeX
@inproceedings{DMSB-gi2000, title = {Anisotropic Feature-Preserving Denoising of Height Fields and Bivariate Data}, author = {Mathieu Desbrun and Mark Meyer and Peter Schr{ and Alan H. Barr}, booktitle = {Proceedings of the Graphics Interface 2000 Conference, May 15-17, 2000, Montr{'{e}}al, Qu{'{e}}bec, Canada}, year = {2000}, month = {May}, pages = {145--152}, url = {http://graphicsinterface.org/wp-content/uploads/gi2000-20.pdf} }
Abstract
In this paper, we present an efficient way to denoise bivariate data like height fields, color pictures or vector fields, while preserving edges and other features. Mixing surface area minimization, graph flow, and nonlinear edge-preservation metrics, our method generalizes previous anisotropic diffusion approaches in image processing, and is applicable to data of arbitrary dimension. Another notable difference is the use of a more robust discrete differential operator, which captures the fundamental surface properties. We demonstrate the method on range images and height fields, as well as greyscale or color images.