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Proceedings Paper

Machine learning for adaptive bilateral filtering
Author(s): Iuri Frosio; Karen Egiazarian; Kari Pulli
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Paper Abstract

We describe a supervised learning procedure for estimating the relation between a set of local image features and the local optimal parameters of an adaptive bilateral filter. A set of two entropy-based features is used to represent the properties of the image at a local scale. Experimental results show that our entropy-based adaptive bilateral filter outperforms other extensions of the bilateral filter where parameter tuning is based on empirical rules. Beyond bilateral filter, our learning procedure represents a general framework that can be used to develop a wide class of adaptive filters.

Paper Details

Date Published: 16 March 2015
PDF: 12 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939908 (16 March 2015); doi: 10.1117/12.2077733
Show Author Affiliations
Iuri Frosio, NVIDIA Corp. (United States)
Karen Egiazarian, NVIDIA Corp. (United States)
Tampere Univ. of Technology (Finland)
Kari Pulli, NVIDIA Corp. (United States)

Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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