On debiasing restoration algorithms: applications to total-variation and nonlocal-means
Statistics Theory
2016-08-08 v1 Statistics Theory
Abstract
Bias in image restoration algorithms can hamper further analysis, typically when the intensities have a physical meaning of interest, e.g., in medical imaging. We propose to suppress a part of the bias -- the method bias -- while leaving unchanged the other unavoidable part -- the model bias. Our debiasing technique can be used for any locally affine estimator including \^a1 regularization, anisotropic total-variation and some nonlocal filters.
Keywords
Cite
@article{arxiv.1503.01587,
title = {On debiasing restoration algorithms: applications to total-variation and nonlocal-means},
author = {Charles-Alban Deledalle and Nicolas Papadakis and Joseph Salmon},
journal= {arXiv preprint arXiv:1503.01587},
year = {2016}
}
Comments
Scale Space and Variational Methods in Computer Vision 2015, May 2015, L{\`e}ge Cap Ferret, France