Machine Learning for Adaptive Bilateral Filtering

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 fi lter outperforms other extensions of the bilateral lter where parameter tuning is based on empirical rules. Beyond bilateral fi lter, our learning procedure represents a general framework that can be used to develop a wide class of adaptive filters.


Karen Egiazarian (NVIDIA)
Kari Pulli (NVIDIA)

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