Tomographic Approach for Sampling Multidimensional Signals with Finite Rate of Innovation

1. Tomographic Approach for Sampling Multidimensional Signals with Finite Rate of Innovation Pancham Shukla and Pier Luigi Dragotti Communications and Signal Processing Group Electrical and Electronic Engineering Department Imperial College, London SW7 2AZ, UK E-mail: {p.shukla, p.dragotti}@imperial.ac.uk This research was funded in part by EPSRC (UK). 1

2. 1. Introduction Samplingis a fundamental step in obtaining sparse representation of signals (e.g. images, video) for applications such as coding, communication, and storage. Shannon’s classical sampling theory considers sampling of bandlimited signals using ‘sinc’ kernel. However, most real-world signals are nonbandlimited, and acquisition devices are non-ideal. Fortunately, recent research on Sampling signals with finite rate of innovation(FRI) [1,2] suggests the ways of sampling and perfect reconstruction of many 1-D nonbandlimited signals (e.g. Diracs, Piecewise polynomials) using a rich class of kernels (e.g. sinc, Gaussian, kernels that reproduce polynomials and exponentials, kernels with rational Fourier transform). The reconstruction is based on annihilating filter method (Prony’s method). 2

3. Signal Kernel Samples Perfect reconstruction ? Contribution:We extend the results of FRI sampling [2] in higher dimensions using compactly supported kernels (e.g. B-splines) that reproduce polynomials (i.e., satisfy Strang-Fix conditions). Earlier, we have shown that it possible to perfectly reconstruct many 2-D nonbandlimited signals (or shapes) from their samples [5]. In sequel to [5], here we show the sampling of more general FRI signals using the connection between Radon projections and moments [3]. 3

4. Input signal Acquisition device Samples Sampling Sampling kernel 2. Sampling Framework The generic 2-D sampling setup (can be extended in n-D as well). 4

5. Sampling kernels and moments from samples We consider the kernels that satisfy Strang-Fix conditions, and therefore, reproduce polynomials up to certain degree That is, where and are the known coefficients. Scaling functions (from wavelet theory) and B-splines are examples of valid kernels. This makes it is possible to retrieve the continuous geometric moments of the original signal from its samples: 5

6. Reproduction of 2-D polynomials of degree 0 and 1 using B-spline kernel Polynomial of degree 0 B-spline of degree 3 Polynomial of degree 1 along y Polynomial of degree 1 along x 6

7. The reconstruction of the original signal is achieved by back-projection. 3. Tomographic Approach Now we consider sampling of FRI signals such as 2-D polynomials with convex polygonal boundaries, and n-D Diracs and bilevel-convex polytopes using Radon transform and annihilating filter method. Radon transform projection of a 2-D function with compact support is given by: In fact, the Radon transform projections are obtained from the observed samples 7

8. 1. Each Radon projection is a 1-D piecewise polynomial of max. degree R and it can be decomposed into a stream of at most N differentiated Diracs using (R+1)-order derivatives. Annihilating Filter based Back-Projection (AFBP) algorithm Consider a case when is a 2-D polynomial of max. degree R-1 inside a convex polygonal closure with N corner points. In this case, we observe that 2. Using Radon-moment connection of [3], we compute the moments of the differentiated Diracs from sample difference 8

9. 3. Using these moments and the annihilating filter method [1], we retrieve Dirac locations and weights and therefore the projection itself [2]. 4. By iterating steps 1, 2 and 3 over N+1 distinct projection angles and then back-projecting the Dirac locations , we retrieve the convex polygonal closure [4]. • From the knowledge of the convex closure and max(R,N+1) Radon projections , we can determine the coefficients , and thus, the polynomial g(x,y) by solving a system of linear equations. Note that the sampling kernel must reproduce polynomials at least up to degree in this case. The AFBP algorithm can be extended for n-dimensional Diracs and bilevel-convex polytopes as well. 9

10. AFBP reconstruction of the 2-D polynomial with convex polygonal boundary. (a) The 2-D polynomial of degree R-1=0 inside convex polygon with N=5 corner points. (b) Radon transform projection Rg(t, theis a 1-D piecewise polynomial signal of degree R=1. (d) Second order derivative of the projection is a stream of N differentiated Diracs:]. 10

11. Simulation: Reconstruction of 2-D polynomial of degree R-1=0. Original signal Samples Reconst. of corner points Difference samples 11

12. 4. References • M Vetterli, P Marziliano, and T Blu, ‘Sampling signals with finite rate of innovation,’ IEEE Trans. Sig. Proc., 50(6): 1417-1428, Jun 2002. • P L Dragotti, M Vetterli, and T Blu, ‘Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets Strang-Fix,’ IEEE Trans. Sig. Proc., Jun 2006, accepted. • P Milanfar, G Verghese, W Karl, and A Willsky, ‘Reconstructing polygons from moments with connections to array processing,’ IEEE Trans. Sig. Proc., 43(2): 432-443, Feb 1995. • I Maravic and M Vetterli, ‘A sampling theorem for the Radon transform of finite complexity objects,’ Proc. IEEE ICASSP, 1197-1200, Orlando, Florida, USA, May 2002. • P Shukla and P L Dragotti, ‘Sampling schemes for 2-D signals with finite rate of innovation using kernels that reproduce polynomials,’ Proc. IEEE ICIP, Genova, Italy, Sep 2005. 12