Recently, it was shown that it is possible to sample classes of signals with finite rate of innovation. These sampling schemes, however, use kernels with infinite support and this leads to complex and instable reconstruction algorithms. In this paper, we show that many signals with finite rate of innovation can be sampled and perfectly reconstructed using kernels of compact support and a local reconstruction algorithm. The class of kernels that we can use is very rich and includes any function satisfying Strang-Fix conditions, Exponential Splines and functions with rational Fourier transforms. Our sampling schemes can be used for either 1-D or 2-D signals with finite rate of innovation.

Date Published: 17 September 2005
PDF: 11 pages
Proc. SPIE 5914, Wavelets XI, 59140Y (17 September 2005); doi: 10.1117/12.616903

Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)