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Stephan Didas
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2020 – today
- 2023
- [i2]Rüdiger Machhamer, Lejla Begic Fazlic, Eray Guven, David Junk, Gunes Karabulut-Kurt, Stefan Naumann, Stephan Didas, Klaus-Uwe Gollmer, Ralph Bergmann, Ingo J. Timm, Guido Dartmann:
Likelihood-based Sensor Calibration for Expert-Supported Distributed Learning Algorithms in IoT Systems. CoRR abs/2309.11526 (2023)
2010 – 2019
- 2019
- [c12]Bernhard Burgeth, Stephan Didas, Andreas Kleefeld:
A Unified Approach to the Processing of Hyperspectral Images. ISMM 2019: 202-214 - 2017
- [j8]Shekoufeh Gorgi Zadeh, Stephan Didas, Maximilian W. M. Wintergerst, Thomas Schultz:
Multi-scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization. J. Math. Imaging Vis. 59(2): 257-269 (2017) - 2016
- [i1]Shekoufeh Gorgi Zadeh, Stephan Didas, Maximilian W. M. Wintergerst, Thomas Schultz:
Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization. CoRR abs/1611.06906 (2016) - 2012
- [p2]Bernhard Burgeth, Luis Pizarro, Stephan Didas, Joachim Weickert:
3D-Coherence-Enhancing Diffusion Filtering for Matrix Fields. Mathematical Methods for Signal and Image Analysis and Representation 2012: 49-63 - 2010
- [j7]Luis Pizarro, Pavel Mrázek, Stephan Didas, Sven Grewenig, Joachim Weickert:
Generalised Nonlocal Image Smoothing. Int. J. Comput. Vis. 90(1): 62-87 (2010)
2000 – 2009
- 2009
- [j6]Stephan Didas, Simon Setzer, Gabriele Steidl:
Combined l2 data and gradient fitting in conjunction with l1 regularization. Adv. Comput. Math. 30(1): 79-99 (2009) - [j5]Stephan Didas, Joachim Weickert, Bernhard Burgeth:
Properties of Higher Order Nonlinear Diffusion Filtering. J. Math. Imaging Vis. 35(3): 208-226 (2009) - [p1]Bernhard Burgeth, Michael Breuß, Stephan Didas, Joachim Weickert:
PDE-based Morphology for Matrix Fields: Numerical Solution Schemes. Tensors in Image Processing and Computer Vision 2009: 125-150 - 2008
- [c11]Luis Pizarro, Bernhard Burgeth, Stephan Didas, Joachim Weickert:
A Generic Neighbourhood Filtering Framework for Matrix Fields. ECCV (3) 2008: 521-532 - 2007
- [j4]Bernhard Burgeth, Stephan Didas, Luc Florack, Joachim Weickert:
A generic approach to diffusion filtering of matrix-fields. Computing 81(2-3): 179-197 (2007) - [j3]Bernhard Burgeth, Andrés Bruhn, Stephan Didas, Joachim Weickert, Martin Welk:
Morphology for matrix data: Ordering versus PDE-based approach. Image Vis. Comput. 25(4): 496-511 (2007) - [c10]Bernhard Burgeth, Stephan Didas, Luc Florack, Joachim Weickert:
A Generic Approach to the Filtering of Matrix Fields with Singular PDEs. SSVM 2007: 556-567 - [c9]Stephan Didas, Joachim Weickert:
Combining Curvature Motion and Edge-Preserving Denoising. SSVM 2007: 568-579 - [c8]Oliver Vogel, Andrés Bruhn, Joachim Weickert, Stephan Didas:
Direct Shape-from-Shading with Adaptive Higher Order Regularisation. SSVM 2007: 871-882 - [c7]Luis Pizarro, Stephan Didas, Frank Bauer, Joachim Weickert:
Evaluating a General Class of Filters for Image Denoising. SCIA 2007: 601-610 - 2006
- [j2]Nils Papenberg, Andrés Bruhn, Thomas Brox, Stephan Didas, Joachim Weickert:
Highly Accurate Optic Flow Computation with Theoretically Justified Warping. Int. J. Comput. Vis. 67(2): 141-158 (2006) - [j1]Gabriele Steidl, Stephan Didas, Julia Neumann:
Splines in Higher Order TV Regularization. Int. J. Comput. Vis. 70(3): 241-255 (2006) - [c6]Stephan Didas, Joachim Weickert:
From Adaptive Averaging to Accelerated Nonlinear Diffusion Filtering. DAGM-Symposium 2006: 101-110 - 2005
- [c5]Stephan Didas, Joachim Weickert, Bernhard Burgeth:
Stability and Local Feature Enhancement of Higher Order Nonlinear Diffusion Filtering. DAGM-Symposium 2005: 451-458 - [c4]Bernhard Burgeth, Stephan Didas, Joachim Weickert:
The Bessel Scale-Space. DSSCV 2005: 84-95 - [c3]Bernhard Burgeth, Stephan Didas, Joachim Weickert:
Relativistic Scale-Spaces. Scale-Space 2005: 1-12 - [c2]Stephan Didas, Bernhard Burgeth, Atsushi Imiya, Joachim Weickert:
Regularity and Scale-Space Properties of Fractional High Order Linear Filtering. Scale-Space 2005: 13-25 - [c1]Gabriele Steidl, Stephan Didas, Julia Neumann:
Relations Between Higher Order TV Regularization and Support Vector Regression. Scale-Space 2005: 515-527
Coauthor Index
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