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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…
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Papers accepted !
My two Phd students will present their latest work at @eusipco2024 in Lyon (congrats!): P.-J. Bénard : Projected Block Coordinate Descent for sparse spike estimation https://hal.science/hal-04462779v1 (accelerating off-the-grid estimation by leveraging the structure of the problem) Antoine Guennec : Adaptive parameter selection for gradient-sparse plus low patch-rank recovery: application to image decomposition https://hal.science/hal-04207313v1 (the first…
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New preprint
We uploaded the huge work of P.-J. Bénard for his PhD: Estimation of off-the-grid sparse spikes with over-parametrized projected gradient descent: theory and application. P.-J. Bénard, Y. Traonmilin, J.-F. Aujol and E. Soubies, 2023. Abstract: “In this article, we study the problem of recovering sparse spikes with overparametrized projected descent. We first provide a theoretical…
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New preprint
We uploaded the last work of Hui Shi during her PhD thesis: Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising, H. Shi, Y. Traonmilin and J.-F. Aujol, 2023. Abstract: “We consider the problem of denoising with the help of prior information taken from a database of clean signals or images.…
Yann Traonmilin
I am a CNRS researcher in the IOP team of the Institut de Mathématiques de Bordeaux.