Paper Info
Title
Sampling schemes for 2-D signals with finite rate of innovation using kernels that reproduce polynomials
Abstract
In this paper, we propose new sampling schemes for classes of 2-D signals with finite rate of innovation (FRI). In particular, we consider sets of 2-D Diracs and bilevel polygons. As opposed to using only sine or Gaussian kernels [I. Maravic et al, 2004], we allow the sampling kernel to be any function that reproduces polynomials. In the proposed sampling schemes, we exploit the polynomial approximation properties of the sampling kernels in association with other relevant techniques such as complex-moments [P. Milanfar et al, 1995], annihilating filter method [M. Vetterli et al, 2002], and directional derivatives. Specifically, for the bilevel polygons, we propose two different methods: the first uses a global reconstruction algorithm and complex moments, while the second is based on directional derivatives and local reconstruction algorithms. The trade-off between these two reconstruction modalities is also briefly discussed.
Year
DOI
Venue
2005
10.1109/ICIP.2005.1530090
Image Processing, 2005. ICIP 2005. IEEE International Conference
Keywords
Field
DocType
Gaussian processes,filtering theory,image reconstruction,image sampling,polynomial approximation,2D signal sampling schemes,Gaussian kernels,bilevel polygons,complex moments,filter method,finite rate of innovation,polynomial approximation,reconstruction algorithm,sampling kernel
Applied mathematics,Polynomial,Reconstruction algorithm,Artificial intelligence,Gaussian process,Directional derivative,Iterative reconstruction,Discrete mathematics,Polygon,Pattern recognition,Gaussian,Sampling (statistics),Mathematics
Conference
Volume
ISSN
ISBN
2
1522-4880
0-7803-9134-9
Citations 
PageRank 
References 
7
1.03
8
Authors
2
Name
Order
Citations
PageRank
Pancham Shukla171.03
Dragotti, P.L.251239.29