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Paper accepted
Congrats to Ali Joundi for his paper Max-sparsity atomic autoencoders with application to inverse problems, A. Joundi, A. Newson, Y. Traonmilin, 2024. Accepted to SSVM 20025 . Abstratc: “An atomic autoencoder is a neural network architecture that decomposes an image as a sum of low dimensional atoms. While it is efficient for image datasets which…
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Workshop Mathématiques de l’IA
Le premier workshop de l’IMB “Mathématiques de l’IA” se déroulera le 26 mai 2025, à l’Institut de Mathématiques de Bordeaux en Salle de Conférence. Le programme complet est disponible :
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Paper acepted
Our paper Joint structure-texture low dimensional modeling for image decomposition with a plug and play framework, A. Guennec, J.- F. Aujol, Y. Traonmilin has been accepted for publication in Siam journal on Imaging Sciences.
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New preprint
We uploaded our new preprint: “Towards optimal algorithms for the recovery of low-dimensional models with linear rates” Y. Traonmilin , J.-F. Aujol, A. Guennec Abstract: We consider the problem of recovering elements of a low-dimensional model from linear measurements. From signal and image processing to inverse problems in data science, this question has been at…
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Paper accepted
Our paper “Sketched over-parametrized projected gradient descent for sparse spike estimation” (https://hal.science/hal-04584951v1) has been accepted to Signal Processing Letters. This is the last work of PJ Bénard for his PhD, his defense is next week! A nice application of compressed sensing in spaces of measures.
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New preprint
“Joint structure-texture low dimensional modeling for image decomposition with a plug and play framework” (Guennec, Aujol, YT) https://hal.science/hal-04648963v1 We describe how structure-texture decomposition is directly linked to the (difficult) design of a regularizer for a complex combination of low dimensional models. Thanks to the PnP approach and DNN, we are able to explicitly design such…
Yann Traonmilin
I am a CNRS researcher in the IOP team of the Institut de Mathématiques de Bordeaux.