Machine learning for adaptive bilateral filtering

Iuri Frosio; Karen Egiazarian; Kari Pulli

doi:10.1117/12.2077733

16 March 2015 Machine learning for adaptive bilateral filtering

Iuri Frosio, Karen Egiazarian, Kari Pulli

Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 939908 (2015) https://doi.org/10.1117/12.2077733
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Iuri Frosio, Karen Egiazarian, and Kari Pulli "Machine learning for adaptive bilateral filtering", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939908 (16 March 2015); https://doi.org/10.1117/12.2077733

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