Modeling Non-Gaussian Noise for Robust Image Analysis
Abstract
Accurate noise models are important to perform reliable robust
image analysis. Indeed, many vision problems can be seen as
parameter estimation problems. In this paper, two noise models are
presented and we show that these models are convenient to
approximate observation noise in different contexts related to image
analysis. In spite of the numerous results on M-estimators, their
robustness is not always clearly addressed in the image analysis
field. Based on Mizera and Muller's recent fundamental work, we
study the robustness of M-estimators for the two presented
noise models, in the fixed design setting. To illustrate the
interest of these noise models, we present two image
vision applications that can be solved within this framework: curves
fitting and edge-preserving image smoothing.
Reference
@inproceedings{jpt-visapp07a,
author = {Ieng, S.-S. and Tarel, J.-P. and Charbonnier, P.},
title = {Modeling Non-Gaussian Noise for Robust Image Analysis},
booktitle = {Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP'07)},
date = {March 8-11},
address = {Barcelona, Spain},
pages = {183-190},
year = {2007},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/visapp07a.html}
}
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