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Communication Dans Un Congrès Année : 2022

Fast off-the-grid sparse recovery with over-parametrized projected gradient descent

Résumé

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.
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Dates et versions

hal-03590939 , version 1 (28-02-2022)
hal-03590939 , version 2 (04-08-2022)

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Citer

Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol. Fast off-the-grid sparse recovery with over-parametrized projected gradient descent. 2022 30th European Signal Processing Conference (EUSIPCO), 2022, Belgrade (Serbia), France. ⟨hal-03590939v2⟩
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