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2. Learning Adaptive Parameter Tuning for Image Processing
 
 # Learning Adaptive Parameter Tuning for Image Processing

  ![](/sites/default/files/styles/wide/public/publications/aANLM__PSNR__p0_0.png?itok=h278dx5o)

 The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content. We propose a simple and flexible method to learn local tuning of parameters in adaptive image processing: we extract simple local features from an image and learn the relation between these features and the optimal filtering parameters. Learning is performed by optimizing a user defined cost function (any image quality metric) on a training set. We apply our method to three classical problems (denoising, demosaicing and deblurring) and we show the effectiveness of the learned parameter modulation strategies. We also show that these strategies are consistent with theoretical results from the literature.



 ## Authors



Jingming Dong (UCLA Vision Lab, University of California, Los Angeles, USA)

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

[Jan Kautz](/person/jan-kautz)

 

 

 ## Publication Date



Sunday, January 28, 2018

 

 ## Published in



[Electronic Imaging 2018, Image Processing: Algorithms and Systems XVI, Burlinga…](https://arxiv.org/abs/1610.09414v2)

 

 ## Research Area



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

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

 

 

 ## Uploaded Files



[Paper](https://research.nvidia.com/sites/default/files/pubs/2018-01_Learning-Adaptive-Parameter//LearningAdaptiveParameterTuningForImageProcessing.pdf "Open file in new window")8.99 MB

[Slides](https://research.nvidia.com/sites/default/files/pubs/2018-01_Learning-Adaptive-Parameter/J_Dong_I_Frosio_J_Kautz_LearningParameterTuningForImageProcessing_EI2018.pdf "Open file in new window")1.72 MB