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CIL

In our unit, we combine physical and mathematical modelling with optimisation and learning techniques to develop robust image reconstruction methods that perform effectively even under limited training data and potentially complex physical models. We focus particularly on modelling spatial distortions, non-Gaussian noise statistics, and the stochastic, time-dependent dynamics that arise in various fluorescence microscopy modalities. Our ultimate objective is to integrate these models into innovative reconstruction methods that fuse physics, optimization, and (deep) learning approaches.

People

  • Luca
    Calatroni

  • Nathan
    Buskulic

  • Christian
    Daniele

  • Joseph
    Arnold

  • Sofia
    Agostoni

Publications

Most recent CIL publications

TitleYearAuthorVenue
Say My Name: a Model’s Bias Discovery Framework2025C Fantasia L Calatroni X Descombes R RekikArXiv Preprint
Box-constrained L0 Bregman-relaxations2025M Essafri L Calatroni E SoubiesArXiv Preprint
Learning Spatially Adaptive l1-Norms Weights for Convolutional Synthesis Regularization2025A Kofler L Calatroni C Kolbitsch K PapafitsorosArXiv Preprint
Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy2024S Bonettini L Calatroni D Pezzi M PratoArXiv Preprint
Exact continuous relaxations of l0-regularized criteria with non-quadratic data terms2024M Essafri L Calatroni E SoubiesHAL