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2. Machine Learning for Adaptive Bilateral Filtering
 
 # Machine Learning for Adaptive Bilateral Filtering

  ![](/sites/default/files/styles/wide/public/pubs/2015-02_Machine-Learning-for/default.jpg?itok=iFOJ8BgI)

 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 lter 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.



 ## Authors



[Iuri Frosio](/person/iuri-frosio)

Karen Egiazarian (NVIDIA)

Kari Pulli (NVIDIA)

 

 

 ## Publication Date



Sunday, February 1, 2015

 

 ## Published in



[Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII](http://spie.org/EI/conferencedetails/image-processing-algorithms-systems#2077733)

 

 ## Research Area



[Computational Photography and Imaging](/research-area/computational-photography-imaging)

[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

 

 

 ## Uploaded Files



[I. Frosio - Machine Learning for Adaptive Bilateral Filtering.pptx](https://research.nvidia.com/sites/default/files/pubs/2015-02_Machine-Learning-for/I.%20Frosio%20-%20Machine%20Learning%20for%20Adaptive%20Bilateral%20Filtering.pptx "Open file in new window")5.13 MB

[Machine Learning for Adaptive Bilateral Filtering.pdf](https://research.nvidia.com/sites/default/files/pubs/2015-02_Machine-Learning-for/Machine%20Learning%20for%20Adaptive%20Bilateral%20Filtering.pdf "Open file in new window")5.05 MB