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Machine learning for adaptive bilateral filtering

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationImage Processing: Algorithms and Systems XIII
PublisherSPIE
Volume9399
ISBN (Print)9781628414899
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventIS&T/SPIE Electronic Imaging / Image Processing: Algorithms and Systems -
Duration: 1 Jan 1900 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
PublisherThe International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceIS&T/SPIE Electronic Imaging / Image Processing: Algorithms and Systems
Period1/01/00 → …

Abstract

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 filter 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.

Keywords

  • Adaptive bilateral filter, Denoising, Machine learning, Optimization, Training

Publication forum classification

Field of science, Statistics Finland