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A Tensor Variational Formulation of Gradient Energy Total Variation
August 29, 2026 Β· Grace Period Β· π Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932, pp. 307-320, Springer, 2015
Authors
Freddie Γ
strΓΆm, George Baravdish, Michael Felsberg
arXiv ID
2608.29172
Category
cs.CV: Computer Vision
Citations
0
Venue
Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932, pp. 307-320, Springer, 2015
Abstract
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 [6] 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)[1] and total variation (TV) [18] for gray-scale and colour images.
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