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On the Relation Between Anisotropic Diffusion and Iterated Adaptive Filtering
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
2008 (English)In: Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008 Proceedings / [ed] Gerhard Rigoll, Springer Berlin/Heidelberg, 2008, 1, , 436-445 p.436-445 p.Conference paper (Refereed)
Abstract [en]

In this paper we present a novel numerical approximation scheme for anisotropic diffusion which is at the same time a special case of iterated adaptive filtering. By assuming a sufficiently smooth diffusion tensor field, we simplify the divergence term and obtain an evolution equation that is computed from a scalar product of diffusion tensor and the Hessian. We propose further a set of filters to approximate the Hessian on a minimized spatial support. On standard benchmarks, the resulting method performs in average nearly as good as the best known denoising methods from the literature, although it is significantly faster and easier to implement. In a GPU implementation video real-time performance is achieved for moderate noise levels.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2008, 1. , 436-445 p.436-445 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 5096
National Category
Computer Science
URN: urn:nbn:se:liu:diva-44878DOI: 10.1007/978-3-540-69321-5_44ISI: 000256932900044Local ID: 78099ISBN: 978-3-540-69320-8 (Print)ISBN: 978-3-540-69321-5 (Online)OAI: diva2:265740
30th DAGM Symposium Munich, Germany, June 10-13, 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-05-04Bibliographically approved

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