ANR/DFG Project SUPREMATIM : SUPerREsolution of 3d MATerials IMages.
Description of the project
Recent and ongoing developments in imaging techniques and computational analysis deeply
modify the way materials sciences and engineering consider their objects of research. Our
project will contribute to this direction of research by developing new superresolution methods
guided by high-resolution local subimages of 3D materials data.
The mathematical methods of choice will be based on local and global Generalized Gaussian
Mixture Models as well as Student-t Mixture Models in conjunction with variational methods.
Appropriate geometrical features related to the engineering topics have to be established to
provide an evaluation platform for the superresolution images, and to be directly involved
into the Bayesian and variational models. The mathematical models will be developed, analyzed
and appropriate ecient algorithms will be derived, including an examination of their
The models will be extended to multimodal images, where due to the size of the structures
of interest, the high resolution image are taken by serial sectioning (FIB-SEM) tomography
and the low resolution images by micro computed tomography. This requires to take the
special acquisition of FIB-SEM tomographic images, in particular curtaining eects and the
anisotropy with respect to the third dimension into account.
A numerical evaluation of the relevance and the benet of the developed superresolution
methods will be performed by comparing the eective properties computed for reactive
in porous media.
Jean-François Aujol, Université Bordeaux, IMB , (professor) (French PI)
Dominique Bernard, CNRS, ICMCB (Director of research)
Yannick Berthoumieu, IPB, IMS (professor)
Johannes Hertrich, Technische Universität Berlin
Lan Nguyen Dang Phuong, Université Bordeaux (PhD student)
Claudia Redenbach, Technische Universität Kaiserslautern (professor)
Abdellatif Saadaldin, Université Bordeaux (PhD student)
Gabriele Steidl, Technische Universität Berlin (professor) (German PI)
Kick-off meeting: 5th and 6th September 2019, ICMCB, Bordeaux
Visio-conference meeting: 18th February 2020
Visio-conference meeting: 17th June 2020
Visio-conference meeting: 20th July 2020
Visio-conference meeting: 9th September 2020
Visio-conference meeting: 21st October 2020
Visio-conference meeting: 23rd March 2021
Visio-conference meeting: 11th May 2021
Visio-conference meeting: 29th June 2021
Visio-conference meeting: 14th September 2021
Visio-conference meeting: 20th October 2021
Visio-conference meeting: 8th December 2021
Visio-conference meeting: 2nd February 2022
Visio-conference meeting: 3rd March 2022
Meeting in Arcachon : 21st June 2022
F. Laus, G. Steidl,
Multivariate myriad filters based on parameter estimation of Student-t distributions,
SIAM Journal on Imaging Sciences, 2019.
- M. Hasanasab, J. Hertrich, F. Laus and G. Steidl.
Alternatives of the EM algorithm for estimating the parameters of the Student-t distribution, Numerical algorithms, 2020.
- M. Hasanasab, J. Hertrich, F. Laus and G. Steidl,
Parseval proximal neuralnetworks, The Journal of Fourier Analysis, 2020.
A. Gastineau, J-F. Aujol, Y. Berthoumieu, and C. Germain,
A residual dense generative adversarial network for pansharpening with geometrical constraints, ICIP 2020.
J. Hertrich, G. Steidl,
Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models, 2020.
Superresolution via Student-t Mixture Models, SIAM Conference on Imaging Science 2020.
J. Hertrich, L. Nguyen, J-F. Aujol, D. Bernard, Y. Berthoumieu, A. Saadaldin and G. Steidl,
PCA Reduced Gaussian Mixture Models with Applications in Superresolution, Inverse Problems in Imaging, 2021.
- A. Gastineau, J-F. Aujol, Y. Berthoumieu, and C. Germain,
Multi-Discriminator with Spectral and Spatial Constraints Adversarial Network for Pansharpening, IEEE Transactions on Geoscience and Remote Sensing, 2021.
- Y. Traonmilin, J-F. Aujol, and A. Leclaire,
The basins of attraction of the global minimizers of non-convex inverse problems with low-dimensional models in infinite dimension, 2020.
P. Hagemann, J. Hertrich, and G. Steidl,
Stochastic Normalizing Flows for Inverse Problems:
a Markov Chains Viewpoints, 2021
- J. Hertrich, A. Houdard, and C. Redenbach,
Wasserstein Patch Prior for Image Superresolution, 2021.
P. Hagemann, J. Hertrich, and G. Steidl,
Generalized Normalizing Flows via Markov Chains, 2021
F. Altekrüger and J. Hertrich,
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution, 2022
Benoit Aune (master 2 student).
Friederike Laus (PhD student).