Wavelet and footprint sampling of signals with a finite rate of innovation

In this paper, we consider classes of not bandlimited signals, namely, streams of Diracs and piecewise polynomial signals, and show that these signals can be sampled and perfectly reconstructed using wavelets as sampling kernel. Due to the multiresolution structure of the wavelet transform, these new sampling theorems naturally lead to the development of a new resolution enhancement algo- rithm based on wavelet footprints [2]. Preliminary results show the potentiality of this algorithm.


Published in:
IEEE Conference on Acoustics, Speech and Signal Processing, 2, 941-944
Presented at:
ICASSP 2004, Montreal, Québec, Canada, 17-21 May 2004
Year:
2004
Laboratories:




 Record created 2005-04-18, last modified 2018-03-17

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