research
∙
08/02/2023
Subgradient Langevin Methods for Sampling from Non-smooth Potentials
This paper is concerned with sampling from probability distributions π o...
share
research
∙
06/29/2023
On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth
We examine the assumption that the hidden-state vectors of recurrent neu...
research
∙
03/16/2023
Learned Discretization Schemes for the Second-Order Total Generalized Variation
The total generalized variation extends the total variation by incorpora...
research
∙
02/21/2023
Learning Gradually Non-convex Image Priors Using Score Matching
In this paper, we propose a unified framework of denoising score-based m...
research
∙
02/20/2023
Bilevel learning of regularization models and their discretization for image deblurring and super-resolution
Bilevel learning is a powerful optimization technique that has extensive...
research
∙
02/16/2023
Explicit Diffusion of Gaussian Mixture Model Based Image Priors
In this work we tackle the problem of estimating the density f_X of a ra...
research
∙
10/25/2022
Stable deep MRI reconstruction using Generative Priors
Data-driven approaches recently achieved remarkable success in medical i...
research
∙
07/13/2022
Is Appearance Free Action Recognition Possible?
Intuition might suggest that motion and dynamic information are key to v...
research
∙
03/23/2022
Computed Tomography Reconstruction using Generative Energy-Based Priors
In the past decades, Computed Tomography (CT) has established itself as ...
research
∙
02/22/2021
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
Electroanatomical maps are a key tool in the diagnosis and treatment of ...
research
∙
02/19/2021
GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models
The personalization of cardiac models is the cornerstone of patient-spec...
research
∙
02/12/2021
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
Recent deep learning approaches focus on improving quantitative scores o...
research
∙
11/12/2020
Shared Prior Learning of Energy-Based Models for Image Reconstruction
We propose a novel learning-based framework for image reconstruction par...
research
∙
10/23/2020
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo
In this work, we propose BP-MVSNet, a convolutional neural network (CNN)...
research
∙
06/15/2020
Total Deep Variation: A Stable Regularizer for Inverse Problems
Various problems in computer vision and medical imaging can be cast as i...
research
∙
03/13/2020
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
It has been proposed by many researchers that combining deep neural netw...
research
∙
01/14/2020
Total Deep Variation for Linear Inverse Problems
Diverse inverse problems in imaging can be cast as variational problems ...
research
∙
12/23/2019
The Five Elements of Flow
In this work we propose five concrete steps to improve the performance o...
research
∙
10/28/2019
Image Morphing in Deep Feature Spaces: Theory and Applications
This paper combines image metamorphosis with deep features. To this end,...
research
∙
10/02/2019
On the estimation of the Wasserstein distance in generative models
Generative Adversarial Networks (GANs) have been used to model the under...
research
∙
07/31/2019
Learned Collaborative Stereo Refinement
In this work, we propose a learning-based method to denoise and refine d...
research
∙
07/29/2019
Self-Supervised Learning for Stereo Reconstruction on Aerial Images
Recent developments established deep learning as an inevitable tool to b...
research
∙
07/19/2019
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
We investigate a well-known phenomenon of variational approaches in imag...
research
∙
05/27/2019
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
We consider the problem of minimizing the sum of submodular set function...
research
∙
04/06/2019
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Backtracking line-search is an old yet powerful strategy for finding bet...
research
∙
04/01/2019
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Following the success of deep learning in a wide range of applications, ...
research
∙
11/09/2018
Learning Energy Based Inpainting for Optical Flow
Modern optical flow methods are often composed of a cascade of many inde...
research
∙
04/09/2018
3D Fluid Flow Estimation with Integrated Particle Reconstruction
The standard approach to densely reconstruct the motion in a volume of f...
research
∙
04/09/2018
Variational 3D-PIV with Sparse Descriptors
3D Particle Imaging Velocimetry (3D-PIV) aim to recover the flow field i...
research
∙
02/13/2018
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow
Wood-composite materials are widely used today as they homogenize humidi...
research
∙
10/04/2017
Semantic 3D Reconstruction with Finite Element Bases
We propose a novel framework for the discretisation of multi-label probl...
research
∙
07/20/2017
Scalable Full Flow with Learned Binary Descriptors
We propose a method for large displacement optical flow in which local m...
research
∙
04/03/2017
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Purpose: To allow fast and high-quality reconstruction of clinical accel...
research
∙
03/15/2017
Real-Time Panoramic Tracking for Event Cameras
Event cameras are a paradigm shift in camera technology. Instead of full...
research
∙
11/30/2016
End-to-End Training of Hybrid CNN-CRF Models for Stereo
We propose a novel and principled hybrid CNN+CRF model for stereo estima...
research
∙
01/23/2016
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
Dense image matching is a fundamental low-level problem in Computer Visi...
research
∙
03/19/2015
On learning optimized reaction diffusion processes for effective image restoration
For several decades, image restoration remains an active research topic ...
research
∙
02/26/2015
Total variation on a tree
We consider the problem of minimizing the continuous valued total variat...
research
∙
04/21/2014
A higher-order MRF based variational model for multiplicative noise reduction
The Fields of Experts (FoE) image prior model, a filter-based higher-ord...
research
∙
04/18/2014
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
In this paper we study an algorithm for solving a minimization problem c...
research
∙
01/16/2014
A bi-level view of inpainting - based image compression
Inpainting based image compression approaches, especially linear and non...
research
∙
01/16/2014
Revisiting loss-specific training of filter-based MRFs for image restoration
It is now well known that Markov random fields (MRFs) are particularly e...
research
∙
01/16/2014
Learning â„“_1-based analysis and synthesis sparsity priors using bi-level optimization
We consider the analysis operator and synthesis dictionary learning prob...
research
∙
01/13/2014
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
This paper addresses a new learning algorithm for the recently introduce...
research
∙
04/26/2013
A Convex Approach for Image Hallucination
In this paper we propose a global convex approach for image hallucinatio...
research
∙
04/26/2013