Dear colleagues,

we would like to cordially invite you to participate the

Workshop: Mathematical Signal and Image Analysis
Date: 12.03.2025 - 14.03.2025
Venue: TUM Science & Study Center Raitenhaslach, Burghausen, Germany.

Invited Speakers are Götz Pfander, Angelica I. Aviles-Rivero, Jan Lellmann, and Francesca Bartolucci.

Minisymposia are organized by
- Johannes Hertrich on Deep Learning for Inverse Problems, and
- Franziska Nestler on Fourier Methods in High-Dimensional Spaces.

There will also be a limited number of contributed talks and a poster session. We are looking forward to your contribution.
 

Thanks to financial support by the Technical University of Munich, we can offer this edition of the workshop free of charge for 50 participants, including coffee breaks and lunches. For hotel accommodation including breakfast, special rates have been negotiated. Details on how to book these rooms will be communicated upon registration. Due to the limited number of spots, we encourage you to register soon, spots will be given out on a first come first served basis.
 

All details can be also found on the conference website 
https://www.math.cit.tum.de/math/forschung/gruppen/data-science/events/mathematical-signal-and-image-analysis/

Please register before January 15nd, 2025.

With best regards,
Brigitte Forster-Heinlein, Felix Krahmer, Stefan Kunis, and Gabriele Steidl

Das Paper  "Stabilizing Tensor Voting for 3D Curvature Estimation" von  Victor Alfaro Pérez, Virginie Uhlmann, und Brigitte Forster-Heinlein ist in den Proceedings "Workshop on mathematical modeling and scientific computing" erschienen: 

https://easychair.org/publications/paper/ssD6

Abstract: Curvature plays an important role in the function of biological membranes, and is therefore a readout of interest in microscopy data. The PyCurv library established itself as a valuable tool for curvature estimation in 3D microscopy images. However, in noisy images, the method exhibits visible instabilities, which are not captured by the standard error measures. In this article, we investigate the source of these instabilities, provide adequate measures to detect them, and introduce a novel post-processing step which corrects the errors. We illustrate the robustness of our enhanced method over various noise regimes and demonstrate that with our orientation correcting post-processing step, the PyCurv library becomes a truly stable tool for curvature quantification.

Das Paper "Rebricking frames and bases" von Thomas Fink, Brigitte Forster und Florian Heinrich ist "online first" im Journal of Mathematical Analysis and Applications erschienen:

https://doi.org/10.1016/j.jmaa.2024.129051

Abstract:

In 1949, Denis Gabor introduced the ``complex signal'' (nowadays called ``analytic signal'') by combining a real function f with its Hilbert transform Hf to a complex function f+iHf. His aim was to extract phase information, an idea that has inspired techniques as the monogenic signal and the complex dual tree wavelet transform. In this manuscript, we consider two questions: When do two real-valued bases or frames {fn:n∈ℕ} and {gn:n∈ℕ} form a complex basis or frame of the form {fn+ign:n∈ℕ}? And for which bounded linear operators A forms {fn+iAfn:n∈ℕ} a complex-valued orthonormal basis, Riesz basis or frame, when {fn:n∈ℕ} is a real-valued orthonormal basis, Riesz basis or frame? We call this approach \emph{rebricking}. It is well-known that the analytic signals don't span the complex vector space L2(ℝ;ℂ), hence H is not a rebricking operator. We give a full characterization of rebricking operators for bases, in particular orthonormal and Riesz bases, Parseval frames, and frames in general. We also examine the special case of finite dimensional vector spaces and show that we can use any real, invertible matrix for rebricking if we allow for permutations in the imaginary part.

Viel Spass beim Lesen!