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A Tensor Variational Formulation of Gradient Energy Total Variation
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-6096-3648
2015 (English)In: ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, EMMCVPR 2015, Springer Berlin/Heidelberg, 2015, Vol. 8932, 307-320 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel variational approach to a tensor-based total variation formulation which is called gradient energy total variation, GETV. We introduce the gradient energy tensor into the GETV and show that the corresponding Euler-Lagrange (E-L) equation is a tensor-based partial differential equation of total variation type. Furthermore, we give a proof which shows that GETV is a convex functional. This approach, in contrast to the commonly used structure tensor, enables a formal derivation of the corresponding E-L equation. Experimental results suggest that GETV compares favourably to other state of the art variational denoising methods such as extended anisotropic diffusion (EAD) and total variation (TV) for gray-scale and colour images.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. Vol. 8932, 307-320 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online)
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-112270DOI: 10.1007/978-3-319-14612-6_23ISI: 000357502000023ISBN: 978-3-319-14612-6 (print)ISBN: 978-3-319-14611-9 (print)OAI: oai:DiVA.org:liu-112270DiVA: diva2:764721
Conference
10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015), 13-16 January 2015, Hong Kong, China
Projects
VIDIVPSEMC^2ETT
Available from: 2014-11-20 Created: 2014-11-20 Last updated: 2016-05-04

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Åström, FreddieBaravdish, GeorgeFelsberg, Michael

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