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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Optimal Choice of Regularization Parameter in Image Denoising
verfasst von : Mirko Lucchese, Iuri Frosio, N. Alberto Borghese
Erschienen in: Image Analysis and Processing – ICIAP 2011
Verlag: Springer Berlin Heidelberg
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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 hyper-surface regularization parameter has little effect on the filtering quality.