Learning Adaptive Parameter Tuning for Image Processing

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...

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Bibliographic Details
Published in:Electronic Imaging Vol. 30; no. 13; pp. 196-1 - 196-8
Main Authors: Dong, Jingming, Frosio, Iuri, Kautz, Jan
Format: Journal Article
Language:English
Published: Society for Imaging Science and Technology 28.01.2018
Subjects:
Adaptive Image Processing
Deblurring
Demosaicing
Denoising
Machine Learning
Non Local Means
ISSN:2470-1173, 2470-1173
Online Access:Get full text
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Summary: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.
Bibliography:2470-1173(20180128)2018:13L.1961;1-
ISSN:2470-1173
2470-1173
DOI:10.2352/ISSN.2470-1173.2018.13.IPAS-196