Reconstruction of signals with sparse representation in optimally dilated Hermite basis
Brajovic, M. B.
; Orovic, I. R
;
Beko, M.B.
; Stankovic, S.
Springer Signal, Image and Video Processing Vol. 1, Nº 1, pp. 1 - 9, January, 2023.
ISSN (print): 1863-1703
ISSN (online): 1863-1711
Scimago Journal Ranking: 0,56 (in 2023)
Digital Object Identifier: 10.1007/s11760-023-02496-0
Abstract
Compressive sensing (CS) provides a set of powerful techniques for the reconstruction of signals with a sparse representation in some particular domain, based on a reduced number of available samples (measurements). The CS application on real-life signals is directly affected by the existence of a basis or a dictionary in which the signal is sparse or highly concentrated (approximately sparse). Motivated by the fact that the time-axis scaling (dilation) factor of Hermite functions (HF) considerably affects the signal sparsity and concentration, in this paper, we propose a CS framework for the signal reconstruction based on a matching pursuit approach using an optimal dilation factor.
