13 citations · 31 across the 9 of their papers we have counts for
9 papers
Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
Christian Heinemann, Freddie Åström, George Baravdish +3
In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel…
A Tensor Variational Formulation of Gradient Energy Total Variation
Freddie Åström, George Baravdish, Michael Felsberg
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…
Mapping-Based Image Diffusion
Freddie Åström, Michael Felsberg, George Baravdish
In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application depen…
On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising
Freddie Åström, George Baravdish, Michael Felsberg
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 term and a smoothness term i…
Targeted Iterative Filtering
Freddie Åström, Michael Felsberg, George Baravdish +1
The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical images require entirely different…
Standalone Neural ODEs with Sensitivity Analysis
Rym Jaroudi, Lukáš Malý, Gabriel Eilertsen +3
This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate g…
Learning via nonlinear conjugate gradients and depth-varying neural ODEs
George Baravdish, Gabriel Eilertsen, Rym Jaroudi +3
The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding t…
Iterative TV minimization on the graph
Japhet Niyobuhungiro, Eric Setterqvist, Freddie Åström +1
We define the space of functions of bounded variation () on the graph. Using the notion of divergence of flows on graphs, we show that the unit ball of the dual space to i…
Damped second order flow applied to image denoising
George Baravdish, Olof Svensson, Mårten Gulliksson +1
In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of…