A Tensor Variational Formulation of Gradient Energy Total Variation

作者:Astrom Freddie*; Baravdish George; Fel**erg Michael
来源:10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2015-01-13 to 2015-01-16.

摘要

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.

  • 出版日期2015