MIMOSA is a currently ongoing research project funded by Agence Nationale de la Recherche from 2017 to 2021 (young researchers / JCJC program, no. ANR-16-CE33-0005-01). MIMOSA lies at the frontier between signal processing and operations research. It aims to propose new optimization strategies, based on mixed integer programming methods, in order to solve exactly some difficult L0-norm-based sparse approximation problems encountered in various signal processing applications.

Publications related to the MIMOSA project

R. Ben Mhenni, S. Bourguignon, J. Ninin and F. Schmidt, Spectral unmixing with sparsity and structuring constraints , in IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands, Sep. 2018. [paper]
M. Boudineau, H. Carfantan and S. Bourguignon, An L0 solution to sparse approximation problems with continuous dictionaries , in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, Apr. 2018.
R. Ben Mhenni, S. Bourguignon, J. Ninin and F. Schmidt, Méthodes exactes de démélange spectral en norme l0 et contraintes de parcimonie structurée à l’aide de MIP , in 19e conférence ROADEF, Société Française de Recherche Opérationnelle et Aide à la Décision, Lorient, France, February 2018. [slides]
R. Ben Mhenni, S. Bourguignon, J. Ninin and F. Schmidt, Démélange parcimonieux exact dans une approche supervisée en imagerie hyperspectrale , in Actes du 26e colloque GRETSI, Juan-les-Pins, Sep. 2017. [paper] [slides]

Publications HAL

8 documents

Communications dans un congrès7 documents

Ramzi Ben Mhenni, Sébastien Bourguignon, Jérôme Idier. A Greedy Sparse Approximation Algorithm Based On L1-Norm Selection Rules. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, Spain. pp.5390-5394, ⟨10.1109/ICASSP40776.2020.9054670⟩. ⟨hal-02563553⟩
Ramzi Ben Mhenni, Sébastien Bourguignon, Marcel Mongeau, Jordan Ninin, Hervé Carfantan. Sparse Branch and Bound for Exact Optimization of L0-Norm Penalized Least Squares. ICASSP 2020, IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona, Spain. pp.ISBN: 978-1-5090-6632-2, ⟨10.1109/ICASSP40776.2020.9053870⟩. ⟨hal-02564594⟩
Ramzi Ben Mhenni, Sébastien Bourguignon, Marcel Mongeau, Jordan Ninin, Hervé Carfantan. Algorithme branch-and-bound pour l’optimisation exacte en norme l0. XXVIIème Colloque francophone de traitement du signal et des images (GRETSI 2019), Aug 2019, Lille, France. ⟨hal-02280754⟩
Ramzi Ben Mhenni, Sébastien Bourguignon, Jordan Ninin, Frédéric Schmidt. SPECTRAL UNMIXING WITH SPARSITY AND STRUCTURING CONSTRAINTS. IEEE Whispers (2018) : 9th Workshop on Hyperspectral Image and Signal Processing, Sep 2018, Amsterdam, Netherlands. ⟨hal-02051443⟩
Mégane Boudineau, Sébastien Bourguignon, Hervé Carfantan. An L0 solution to sparse approximation problems with continuous dictionaries. IEEE ICASSP, Apr 2018, Calgary, Canada. ⟨hal-02052951⟩
Ramzi Ben Mhenni, Sébastien Bourguignon, Jordan Ninin, Frédéric Schmidt. Méthodes exactes de démélange spectral en norme L0 et contraintes de parcimonie structurée à l'aide de MIP. 19ème congrès de la ROADEF (2018), Feb 2018, Lorient, France. ⟨hal-02051460⟩
Ramzi Ben Mhenni, Sébastien Bourguignon, Jordan Ninin, Frédéric Schmidt. Démélange parcimonieux exact dans une approche supervisée en imagerie hyperspectrale. colloque GRETSI 2017, Sep 2017, Juan les Pins, France. ⟨hal-02051435⟩

Habilitations à diriger des recherches1 document

Sébastien Bourguignon. Modèles et algorithmes dédiés pour la résolution de problèmes inverses parcimonieux en traitement du signal et de l'image. Traitement du signal et de l'image [eess.SP]. Université de Nantes, 2020. ⟨tel-02583976⟩