Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Andreas Weinmann"'
Publikováno v:
Mathematics, Vol 10, Iss 18, p 3278 (2022)
Magnetic Particle Imaging is an imaging modality that exploits the non-linear magnetization response of superparamagnetic nanoparticles to a dynamic magnetic field. In the multivariate case, measurement-based reconstruction approaches are common and
Externí odkaz:
https://doaj.org/article/da309d6ca94b43a982de2958a9766dac
Publikováno v:
Electronics; Volume 12; Issue 10; Pages: 2197
The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::708cf6acf97feed6fcc3a612d9e3698e
https://doi.org/10.3390/electronics12102197
https://doi.org/10.3390/electronics12102197
Publikováno v:
Image Processing On Line. 10:124-149
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8deebbb128212b47303144ee637d3f2b
https://doi.org/10.5201/ipol.2020.295
https://doi.org/10.5201/ipol.2020.295
Publikováno v:
SIAM Journal on Imaging Sciences. 13:2307-2360
Mumford--Shah models are well-established and powerful variational tools for the regularization of noisy data. In the case of images this includes regularizing both the edge set as well as the imag...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ca27c17b0fffa5c3118b083e6f1aa25
https://doi.org/10.1137/19m1300959
https://doi.org/10.1137/19m1300959
Autor:
Andreas Weinmann, Martin Storath
Publikováno v:
Multiscale Model. Simul. 18, 674-706 (2020)
In this paper, we consider the sparse regularization of manifold-valued data with respect to an interpolatory wavelet/multiscale transform. We propose and study variational models for this task and provide results on their well-posedness. We present
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd74016c82387476611dbc4206f925e1
https://doi.org/10.1137/19m1249801
https://doi.org/10.1137/19m1249801
Autor:
Andreas Weinmann, Ulrich Reif
Publikováno v:
Advances in Computational Mathematics. 47
We consider geometric Hermite subdivision for planar curves, i.e., iteratively refining an input polygon with additional tangent or normal vector information sitting in the vertices. The building block for the (nonlinear) subdivision schemes we propo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c36534a86ded6d8427d0724cdd8e611
https://doi.org/10.1007/s10444-021-09876-5
https://doi.org/10.1007/s10444-021-09876-5
We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral X-ray computed tomography (CT). Our aim is to exploit the strong structural correlatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36e32a565e4e1ade093216fd095a7332
http://arxiv.org/abs/2009.05814
http://arxiv.org/abs/2009.05814
Signals and images with discontinuities appear in many problems in such diverse areas as biology, medicine, mechanics, and electrical engineering. The concrete data are often discrete, indirect and noisy measurements of some quantities describing the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aee778ae96065b3e16d625a8c7d40d17
https://opus4.kobv.de/opus4-h-da/frontdoor/index/index/docId/382
https://opus4.kobv.de/opus4-h-da/frontdoor/index/index/docId/382
Autor:
Martin Holler, Andreas Weinmann
Publikováno v:
Handbook of Variational Methods for Nonlinear Geometric Data ISBN: 9783030313500
Many methods for processing scalar and vector valued images, volumes and other data in the context of inverse problems are based on variational formulations. Such formulations require appropriate regularization functionals that model expected propert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51315ed64b54ef16654d11bca690ca4f
https://doi.org/10.1007/978-3-030-31351-7_2
https://doi.org/10.1007/978-3-030-31351-7_2
Autor:
Andreas Weinmann, Maximilian Baust
Publikováno v:
Handbook of Variational Methods for Nonlinear Geometric Data ISBN: 9783030313500
The last decade has witnessed a considerable amount of research being devoted on how to process big and often unstructured data. However, one often neglects the fact that a considerable portion of today’s data deluge is actually structured, particu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ef42ea81f5ebb49029f4f588cbb6ad4
https://doi.org/10.1007/978-3-030-31351-7_22
https://doi.org/10.1007/978-3-030-31351-7_22