Search Results for author: Jan Lellmann

Found 13 papers, 5 papers with code

On the Connection between Dynamical Optimal Transport and Functional Lifting

no code implementations6 Jul 2020 Thomas Vogt, Roland Haase, Danielle Bednarski, Jan Lellmann

By modifying the continuity equation, the approach can also be extended to models with higher-order regularization.

Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition

1 code implementation10 Jan 2020 Roland Haase, Stefan Heldmann, Jan Lellmann

Low-rank and sparse decompositions and robust PCA (RPCA) are highly successful techniques in image processing and have recently found use in groupwise image registration.

Image Registration

Fully-deformable 3D image registration in two seconds

no code implementations17 Dec 2018 Daniel Budelmann, Lars König, Nils Papenberg, Jan Lellmann

We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU).

Image Registration

Higher-Order Total Directional Variation. Part I: Imaging Applications

1 code implementation12 Dec 2018 Simone Parisotto, Jan Lellmann, Simon Masnou, Carola-Bibiane Schönlieb

We introduce a new class of higher-order total directional variation regularizers.

Numerical Analysis 47A52, 49M30, 49N45, 65J22, 94A08

A multi-contrast MRI approach to thalamus segmentation

1 code implementation27 Jul 2018 Veronica Corona, Jan Lellmann, Peter Nestor, Carola-Bibiane Schoenlieb, Julio Acosta-Cabronero

Thalamic alterations are relevant to many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis.

Semantic Segmentation

A matrix-free approach to parallel and memory-efficient deformable image registration

no code implementations27 Apr 2018 Lars König, Jan Rühaak, Alexander Derksen, Jan Lellmann

Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic.

Image Registration

Image reconstruction with imperfect forward models and applications in deblurring

no code implementations3 Aug 2017 Yury Korolev, Jan Lellmann

In this approach, errors in the data and in the forward models are described using order intervals.

Deblurring Image Reconstruction

A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery

no code implementations24 Jan 2017 Juheon Lee, David Coomes, Carola-Bibiane Schonlieb, Xiaohao Cai, Jan Lellmann, Michele Dalponte, Yadvinder Malhi, Nathalie Butt, Mike Morecroft

Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case).

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

1 code implementation7 Apr 2016 Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Jan Lellmann, Daniel Cremers

Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.

Color Image Denoising Image Denoising +1

Sublabel-Accurate Relaxation of Nonconvex Energies

1 code implementation CVPR 2016 Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers

We propose a novel spatially continuous framework for convex relaxations based on functional lifting.

Solving QVIs for Image Restoration with Adaptive Constraint Sets

no code implementations3 Jul 2014 Frank Lenzen, Jan Lellmann, Florian Becker, Christoph Schnörr

In the present paper we prove uniqueness for a larger class of problems and in particular independent of the image size.

Image Restoration

Imaging with Kantorovich-Rubinstein discrepancy

no code implementations1 Jul 2014 Jan Lellmann, Dirk A. Lorenz, Carola Schönlieb, Tuomo Valkonen

We propose the use of the Kantorovich-Rubinstein norm from optimal transport in imaging problems.

Image Denoising

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

no code implementations2 Apr 2014 Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

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