Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415045 | Computational Statistics & Data Analysis | 2012 | 16 Pages |
Abstract
It is shown how to choose the smoothing parameter in image denoising by a statistical multiresolution criterion, both globally and locally. Using inhomogeneous diffusion and total variation regularization as examples for localized regularization schemes, an efficient method for locally adaptive image denoising is presented. As expected, the smoothing parameter serves as an edge detector in this framework. Numerical examples together with applications in confocal microscopy illustrate the usefulness of the approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Thomas Hotz, Philipp Marnitz, Rahel Stichtenoth, Laurie Davies, Zakhar Kabluchko, Axel Munk,