Spikes super-resolution with random Fourier sampling
Résumé
We leverage recent results from machine learning to show theoretically and practically that it is possible to stably recover a signal made of few spikes (in the gridless setting) from few random weighted Fourier measurements. Given a free choice of frequencies, a number of measurements lower than with the traditional low-pass filter (uniform sampling of low frequencies) guarantees stable recovery.
| Origine | Fichiers produits par l'(les) auteur(s) |
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