Anisotropic Nonlocal Means Denoising
Statistics Theory
2012-12-04 v2 Information Theory
math.IT
Statistics Theory
Abstract
It has recently been proved that the popular nonlocal means (NLM) denoising algorithm does not optimally denoise images with sharp edges. Its weakness lies in the isotropic nature of the neighborhoods it uses to set its smoothing weights. In response, in this paper we introduce several theoretical and practical anisotropic nonlocal means (ANLM) algorithms and prove that they are near minimax optimal for edge-dominated images from the Horizon class. On real-world test images, an ANLM algorithm that adapts to the underlying image gradients outperforms NLM by a significant margin.
Keywords
Cite
@article{arxiv.1112.0311,
title = {Anisotropic Nonlocal Means Denoising},
author = {Arian Maleki and Manjari Narayan and Richard G. Baraniuk},
journal= {arXiv preprint arXiv:1112.0311},
year = {2012}
}
Comments
Accepted for publication in Applied and Computational Harmonic Analysis (ACHA)