Publikationen von Laurent Hoeltgen
L. Hoeltgen, P. Peter, M. Breuß:
Clustering-Based Quantisation for PDE-Based Image Compression.
Signal, Image and Video Processing, Volume 12(3), pp. 411–419, March 2018, Springer London.- L. Hoeltgen, A. Kleefeld, I. Harris , M. Breuß:
Theoretical Foundation of the Weighted Laplace Inpainting Problem.
Brandenburgische Technische Universität Cottbus-Senftenberg, Cottbus, Germany, 2018.
- G. Radow, L. Hoeltgen, Y. , M. Breuß:
Optimisation of photometric stereo methods by non-convex variational minimisation.
Brandenburgische Technische Universität Cottbus-Senftenberg, Cottbus, Germany, 2017.
- M. Breuß, L. Hoeltgen, A. Kleefeld:
Matrix-Valued Levelings for Colour Images.
In J. Angulo, S. Velasco-Forero, F. Meyer (Eds.): Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2017), LNCS Vol. 10225, pp. 296-308, Springer, 2017.
- L. Hoeltgen, I. Harris , M. Breuß, A. Kleefeld:
Analytic Existence and Uniqueness Results for PDE-Based Image Reconstruction with the Laplacian.
In A. B. Dahl, Y. Dong, F. Lauze (Eds.): Proc. 6th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2017, Kolding, Denmark, June 2017), LNCS Vol. 10302, pp. 66-79, Springer, 2017.
- G. Radow, M. Breuß, L. Hoeltgen, T. Fischer:
Optimised Anisotropic Poisson Denoising.
In P. Sharma, F. M. Bianchi (Eds.): Image Analysis, Proc. 20th Scandinavian Conference (SCIA 2017, Tromsø Norway, June 2017), Lecture Notes in Computer Science, Volume 10269, pp. 502-514, Springer, Berlin, 2017.
- R. Dachsel, M. Breuß, L. Hoeltgen:
Shape Matching by Time Integration of Partial Differential Equations.
In A. B. Dahl, Y. Dong, F. Lauze (Eds.): Proc. 6th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2017, Kolding, Denmark, June 2017), LNCS Vol. 10302, pp. 669-680, Springer, 2017.
- L. Hoeltgen, M. Breuß, G. Herold, E. Sarradj:
Sparse l1 Regularisation of Matrix Valued Models for Acoustic Source Characterisation.
Optimization and Engineering, Volume 19, Issue 1, pp. 39–70, May 2017, Springer US.
- L. Hoeltgen:
Understanding Image Inpainting with the Help of the Helmholtz Equation.
In K. Maleknejad (Eds.): Mathematical Sciences, Volume 11, Issue 1, pp. 73–77, March 2017, Springer Berlin Heidelberg.
- M. Breuß, L. Hoeltgen, A. S. Boroujerdi, A. M. Yarahmadi:
Highly Robust Clustering of GPS Driver Data for Energy Efficient Driving Style Modelling.
Brandenburgische Technische Universität Cottbus-Senftenberg, Cottbus, Germany, 2016
- L. Hoeltgen, Y. Queau, M. Breuß, G. Radow:
Optimised photometric stereo via non-convex variational minimisation.
In R. Wilson, E. Hancock, W. Smith (Eds.): Proc. 27th British Machine Vision Conference (BMVC 2016, York, UK, September 2016), BMVA Press, 2016.
- L. Hoeltgen, M. Breuß:
Efficient Co-domain Quantisation for PDE-based Image Compression.
In A. Handlovičová and D. Ševčovič (Eds.): Proceedings of the Conference Algoritmy 2016, 20th Conference on Scientific Computing, pp. 194-203
- L. Hoeltgen, M. Mainberger, S. Hoffmann, J. Weickert, C.-H. Tang, S. Setzer, D. Johannsen, F. Neumann, B. Doerr:
Optimising Spatial and Tonal Data for PDE-based Inpainting.
In B. Maitine, G. Peyré, C. Schnörr, J.-B. Caillau, T. Haberkorn (Eds.): Variational Methods, In Imaging and Geometric Control, De Gruyter, 2016, in press.
- P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
From Optimised Inpainting with Linear PDEs Towards Competitive Image Compression Codecs.
In T. Bräunl, B. McCane, M. Rivers, X. Yu (Eds.): Image and Video Technology, Lecture Notes in Computer Science, Volume 9431, Springer International Publishing, 2016, pp. 63-74
- P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
Evaluating the True Potential of Diffusion Based Inpainting in a Compression Context.
Saarland University, Faculty 6.1 - Mathematics, Saarbrücken, Germany, 2016
- L. Hoeltgen, M. Breuß:
Bregman Iteration for Correspondence Problems: A Study of Optical Flow.
Brandenburgische Technische Universität Cottbus-Senftenberg, Cottbus, Germany, 2015
- L. Hoeltgen, J. Weickert:
Why does non-binary mask optimisation work for diffusion-based image compression?
In X.-C. Tai, E. Bae, T. F. Chan, S. Y. Leung, M. Lysaker (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015),Lecture Notes in Computer Science (LNCS), Volume 8932, 85-98 Springer, 2015
- L. Hoeltgen, S. Setzer, J. Weickert:
An Optimal Control Approach to Find Sparse Data for Laplace Interpolation.
In A. Heyden, F. Kahl, C. Olsson, M. Oskarsson, X.-C. Tai (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), Lecture Notes in Computer Science (LNCS), Volume 8081, 151-164, Springer, Heidelberg, 2013
- L. Hoeltgen, S. Setzer, M. Breuß:
Intermediate Flow Field Filtering in Energy Based Optic Flow Computations.
In Y. Boykov, F. Kahl, V. Lempitsky, F.R. Schmidt (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2011), Lecture Notes in Computer Science (LNCS), Volume 6819, 315-328, Springer, Berlin, 2011.