arXiv:2608.22302v1 Announce Type: new
Abstract: The case when a partial differential equation (PDE) can be considered as an Euler-Lagrange (E-L) equation of an energy functional, consisting of a data...
By Freddie {\AA}str\"om, George Baravdish, Michael Felsberg
arXiv:2608.29172v1 Announce Type: new
Abstract: We present a novel variational approach to a tensor-based total variation formulation which is called gradient energy total variation, GETV. We introdu...
By Freddie {\AA}str\"om, George Baravdish, Michael Felsberg
arXiv:2608.29227v1 Announce Type: new
Abstract: In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion updat...
By Christian Heinemann, Freddie {\AA}str\"om, George Baravdish, Kai Krajsek, Michael Felsberg, Hanno Scharr
The paper introduces a novel nonlinear diffusion scheme for image denoising, derived from a linear diffusion process applied in a value space tailored to specific application domains such as image compression, still-image acquisition, and medical imaging. It argues that this application-driven linear diffusion in the transformed space outperforms existing nonlinear diffusion techniques.
By Freddie {\AA}str\"om, Michael Felsberg, George Baravdish, Claes Lundstr\"om
arXiv:2609.25429v1 Announce Type: new
Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that...
By Mahmoud Saeedi Kelishami
arXiv:2605.11585v2 Announce Type: replace
Abstract: This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtre...
By Shota Saito, Yuta Nakahara, Kohei Horinouchi, Naoki Ichijo, Manabu Kobayashi, Toshiyasu Matsushima