Fast off-the-grid sparse recovery with over-parametrized projected gradient descent
Langue
en
Communication dans un congrès
Ce document a été publié dans
2022 30th European Signal Processing Conference (EUSIPCO), 2022, Belgrade (Serbia).
Résumé en anglais
We consider the problem of recovering off-the-grid spikes from Fourier measurements. Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching pursuit (OMP) iteratively add spikes to the solution ...Lire la suite >
We consider the problem of recovering off-the-grid spikes from Fourier measurements. Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching pursuit (OMP) iteratively add spikes to the solution then perform a costly (when the number of spikes is large) descent on all parameters at each iteration. In 2D, it was shown that performing a projected gradient descent (PGD) from a gridded over-parametrized initialization was faster than continuous orthogonal matching pursuit. In this paper, we propose an off-the-grid over-parametrized initialization of the PGD based on OMP that permits to fully avoid grids and gives faster results in 3D.< Réduire
Mots clés en anglais
spike super-resolution
non-convex optimization
over-parametrization
projected gradient descent
continuous orthogonal matching pursuit
Project ANR
Régularisation performante de problèmes inverses en grande dimension pour le traitement de données - ANR-20-CE40-0001
Origine
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