Optimal choice of regularization parameter in image denoising

Authors:
Mirko Lucchese;Iuri Frosio;N. Alberto Borghese
Affiliations:
Applied Intelligent System Laboratory, Computer Science Dept., University of Milan, Milan Italy;Applied Intelligent System Laboratory, Computer Science Dept., University of Milan, Milan Italy;Applied Intelligent System Laboratory, Computer Science Dept., University of Milan, Milan Italy
Venue:
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Year:
2011

Citing 3
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Abstract

The Bayesian approach applied to image denoising gives rise to a regularization problem. Total variation regularizers have been introduced with the motivation of being edge preserving. However we show here that this may not always be the best choice in images with low/medium frequency content like digital radiographs. We also draw the attention on the metric used to evaluate the distance between two images and how this can influence the choice of the regularization parameter. Lastly, we show that hypersurface regularization parameter has little effect on the filtering quality.