Hochschule Würzburg-Schweinfurt

Martin Storath

Anschrift:

Leistungsprofil:

• Angewandte Mathematik
• Bildverarbeitung
• Computer Vision
• Maschinelles Lernen
• Data Science Digitalisierung

Publikationen:

• 30. L. Kiefer, M. Storath, A. Weinmann. Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case. Foundations of Computational Mathematics, to appear 29. M. Storath, A. Weinmann. Variational regularization of inverse problems for manifold-valued data. Information and Inference: A Journal of the IMA, to appear 28. M. Storath, A. Weinmann. Wavelet sparse regularization for manifold-valued data. Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 18(2):674–706, 2020 27. L. Kiefer, M. Storath, A. Weinmann. An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting. IEEE Transactions on Image Processing, 29:921–933, 2019 26. M. Storath, L. Kiefer, A. Weinmann. Smoothing for signals with discontinuities using higher order Mumford-Shah models. Numerische Mathematik, 143(2):423-460, 2019 25. M. Esposito, C. Hennersperger, R. Göbl, L. Demaret, M. Storath, N. Navab, M. Baust, A. Weinmann. Total variation regularization of pose signals with an application to 3D freehand ultrasound. IEEE Transactions on Medical Imaging, 38(10):2245–2258, 2019 24. A. Bendinger, C. Debus, C. Glowa, C. Karger, J. Peter, M. Storath. Bolus arrival time estimation in dynamic contrast-enhanced MRI of small animals based on spline models. Physics in Medicine and Biology, 64(4):045003, 2019 23. D. Fortun, M. Storath, D. Rickert, A. Weinmann, M. Unser. Fast piecewise-affine motion estimation without segmentation. IEEE Transactions on Image Processing, 27(11):5612–5624, 2018 22. K. Bredies, M. Holler, M. Storath, A. Weinmann. Total generalized variation for manifold-valued data. SIAM Journal on Imaging Sciences, 11(3):1785–1848, 2018 21. W. Erb, A. Weinmann, M. Ahlborg, C. Brandt, G. Bringout, T. Buzug, J. Frikel, C. Kaethner, T. Knopp, T. März, M. Möddel, M. Storath, A. Weber. Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems, 34(5):055012, 2018 20. M. Kiechle, M. Storath, A. Weinmann, M. Kleinsteuber. Model-based learning of local image features for unsupervised texture segmentation. IEEE Transactions on Image Processing, 27(4):1994–2007, 2018 19. M. Storath, A. Weinmann. Fast median filtering for phase or orientation data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3):639–652, 2018 18. M. Storath, D. Rickert, M. Unser, A. Weinmann. Fast segmentation from blurred data in 3D fluorescence microscopy. IEEE Transactions on Image Processing, 26(10):4856–4870, 2017 17. M. Storath, A. Weinmann, M. Unser. Jump-penalized least absolute values estimation of scalar or circle-valued signals. Information and Inference: A Journal of the IMA, 6(3):225–245, 2017 16. M. Storath, L. Demaret, P. Massopust. Signal analysis based on complex wavelet signs. Applied and Computa- tional Harmonic Analysis, 42(2):199–223, 2017 15. M. Storath, C. Brandt, M. Hofmann, T. Knopp, J. Salamon, A. Weber, A. Weinmann. Edge preserving and noise reducing reconstruction for magnetic particle imaging. IEEE Transactions on Medical Imaging, 36(1):74–85, 2017 14. M. Baust, A. Weinmann, M. Wieczorek, T. Lasser, M. Storath, N. Navab. Combined tensor fitting and TV regularization in diffusion tensor imaging based on a Riemannian manifold approach. IEEE Transactions on Medical Imaging, 35(8):1972–1989, 2016 13. A. Stefanoiu, A. Weinmann, M. Storath, N. Navab, M. Baust. Joint segmentation and shape regularization with a generalized forward backward algorithm. IEEE Transactions on Image Processing, 25(7):3384–3394, 2016 12. A. Weinmann, L. Demaret, M. Storath. Mumford-Shah and Potts regularization for manifold-valued data. Journal of Mathematical Imaging and Vision, 55(3):428–445, 2016 11. M. Storath, A. Weinmann, M. Unser. Exact algorithms for L1-TV regularization of real-valued or circle-valued signals. SIAM Journal on Scientific Computing, 38(1):A614–A630, 2016 10. K. Hohm, M. Storath, A. Weinmann. An algorithmic framework for Mumford-Shah regularization of inverse problems in imaging. Inverse Problems, 31(11):115011, 2015 − Selected as “Highlight” publication by the editorial board − 9. A. Weinmann, M. Storath. Iterative Potts and Blake-Zisserman minimization for the recovery of functions with discontinuities from indirect measurements. Proceedings of the Royal Society A, 471(2176), 2015 8. X. Cai, J. Fitschen, M. Nikolova, G. Steidl, M. Storath. Disparity and optical flow partitioning using extended Potts priors. Information and Inference, 4(1):43–62, 2015 M. Storath, A. Weinmann, J. Frikel, and M. Unser. Joint image reconstruction and segmentation using the Potts model. Inverse Problems, 31(2):025003, 2015 − Selected as “Highlight” publication by the editorial board − 6. A. Weinmann, M. Storath, L. Demaret. The L1-Potts functional for robust jump-sparse reconstruction. SIAM Journal on Numerical Analysis, 53(1):644–673, 2015 5. A. Weinmann, L. Demaret, M. Storath. Total variation regularization for manifold-valued data. SIAM Journal on Imaging Sciences, 7(4):2226–2257, 2014 4. M. Storath, A. Weinmann. Fast partitioning of vector-valued images. SIAM Journal on Imaging Sciences, 7(3):1826–1852, 2014 3. M. Storath, A. Weinmann, L. Demaret. Jump-sparse and sparse recovery using Potts functionals. IEEE Trans- actions on Signal Processing, 62(14):3654–3666, 2014 2. M. Storath. Directional multiscale amplitude and phase decomposition by the monogenic curvelet transform. SIAM Journal on Imaging Sciences, 4(1):57–78, 2011 1. S. Held, M. Storath, P. Massopust, B. Forster. Steerable wavelet frames based on the Riesz transform. IEEE Transactions on Image Processing, 19(3):653–667, 2010