Learning Adaptive Parameter Tuning for Image Processing
This addresses the need for adaptive image processing in computer vision, but it is incremental as it builds on classical methods with learned parameter modulation.
The paper tackled the problem of adaptive image processing by learning local parameter tuning from simple features, achieving effective results in denoising, demosaicing, and deblurring tasks.
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.