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 convergence behavior. 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 e ects and the anisotropy with respect to the third dimension into account. A numerical evaluation of the relevance and the bene t of the developed superresolution methods will be performed by comparing the e ective properties computed for reactive ow in porous media.

 

People involved

 
  • 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 (PhD student)
  • Théo Mauger, Université Bordeaux (master student)
  • 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)

    •  

    Events

     
  • 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
  • Meeting in Bordeaux : 17th January 2023
  • Meeting in Berlin : 10th March 2023
  • Visio-conference meeting: 8th September 2023
  • Visio-conference meeting: 9th January 2024
  • Visio-conference meeting: 16th April 2024

    •  

    Publications

     
    1. F. Laus, G. Steidl, Multivariate myriad filters based on parameter estimation of Student-t distributions, SIAM Journal on Imaging Sciences, 2019.
    2. 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.
    3. M. Hasanasab, J. Hertrich, F. Laus and G. Steidl, Parseval proximal neuralnetworks, The Journal of Fourier Analysis, 2020.
    4. A. Gastineau, J-F. Aujol, Y. Berthoumieu, and C. Germain, A residual dense generative adversarial network for pansharpening with geometrical constraints, ICIP 2020.
    5. J. Hertrich, G. Steidl, Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models, 2020.
    6. J. Hertrich, Superresolution via Student-t Mixture Models, SIAM Conference on Imaging Science 2020.
    7. 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.
    8. 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.
    9. The basins of attraction of the global minimizers of non-convex inverse problems with low-dimensional models in infinite dimension,
      Y. Traonmilin, J-F. Aujol, and A. Leclaire, Information and Inference, 2022.
    10. P. Hagemann, J. Hertrich, and G. Steidl, Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoints, 2021
    11. J. Hertrich, A. Houdard, and C. Redenbach, Wasserstein Patch Prior for Image Superresolution, 2021.
    12. P. Hagemann, J. Hertrich, and G. Steidl, Generalized Normalizing Flows via Markov Chains, 2021
    13. F. Altekrüger and J. Hertrich, WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution, 2022
    14. Image super-resolution with PCA Reduced generalized Gaussian mixture models,
      L. Nguyen, J. Hertrich, J-F. Aujol, Y. Berthoumieu, Inverse Problems and Imaging, volume 17, number 6, pp. 1165-1192, 2023.

     

    Past members

     
  • Benoit Aune (master 2 student).
  • Friederike Laus (PhD student).