Projected gradient descent for non-convex sparse spike estimation
Language
en
Article de revue
This item was published in
IEEE Signal Processing Letters. 2020, vol. 27, p. 1110 - 1114
Institute of Electrical and Electronics Engineers
English Abstract
We propose a new algorithm for sparse spike estimation from Fourier measurements. Based on theoretical results on non-convex optimization techniques for off-the-grid sparse spike estimation, we present a projected gradient ...Read more >
We propose a new algorithm for sparse spike estimation from Fourier measurements. Based on theoretical results on non-convex optimization techniques for off-the-grid sparse spike estimation, we present a projected gradient descent algorithm coupled with a spectral initialization procedure. Our algorithm permits to estimate the positions of large numbers of Diracs in 2d from random Fourier measurements. We present, along with the algorithm, theoretical qualitative insights explaining the success of our algorithm. This opens a new direction for practical off-the-grid spike estimation with theoretical guarantees in imaging applications.Read less <
English Keywords
non-convex optimization
spike super-resolution
projected gradient descent
Origin
Hal imported