Simulation of Gegenbauer processes using wavelet packets

In this paper, we propose to study the synthesis of Gegenbauer processes using the wavelet packets transform. In order to simulate 1-factor Gegenbauer process, we introduce an original algorithm, inspired by the one proposed by Coifman and Wickerhauser [CW92], to adaptively search for the best-ortho-basis in the wavelet packet library where the covariance matrix of the transformed process is nearly diagonal. Our method clearly outperforms the one recently proposed by [Whi01], is very fast, does not depend on the wavelet choice, and is not very sensitive to the length of the time series. From these first results we propose an algorithm to build bases to simulate k-factor Gegenbauer processes. Given the simplicity of programming and running, we feel the general practitioner will be attracted to our simulator. Finally we evaluate the approximation due to the fact that we consider the wavelet packet coeficients as uncorrelated. An empirical study is carried out which supports our results.Gegenbauer process, Wavelet packet transform, Best-basis, Autocovariance

Nonlinear Multilayered Representation of Graph-Signals

We propose a nonlinear multiscale decomposition of signals defined on the vertex set of a general weighted graph. This decomposition is inspired by the hierarchical multiscale (BV, L 2) decomposition of Tadmor, Nezzar, and Vese (Multiscale Model. Simul. 2(4):554–579, 2004). We find the decomposition by iterative regularization using a graph variant of the classical total variation regularization (Rudin et al, Physica D 60(1–4):259–268, 1992). Using tools from convex analysis, and in particular Moreau’s identity, we carry out the mathematical study of the proposed method, proving the convergence of the representation and providing an energy decomposition result. The choice of the sequence of scales is also addressed. Our study shows that the initial scale can be related to a discrete version of Meyer’s norm (Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, 2001) which we introduce in the present paper. We propose to use the recent primal-dual algorithm of Chambolle and Pock (J. Math. Imaging Vis. 40:120–145, 2011) in order to compute both the minimizer of the graph total variation and the corresponding dual norm. By applying the graph model to digital images, we investigate the use of nonlocal methods to the multiscale decomposition task. Since the only assumption needed to apply our method is that the input data is living on a graph, we are also able to tackle the task of adaptive multi

Multichannel Poisson Denoising and Deconvolution on the Sphere : Application to the Fermi Gamma Ray Space Telescope

International audienceA multiscale representation-based denoising method for spherical data contaminated with Poisson noise, the multiscale variance stabilizing transform on the sphere (MS-VSTS), has been recently proposed. This paper first extends this MS-VSTS to spherical 2D-1D, where the two first dimensions are longitude and latitude, and the third dimension is a meaningful physical index such as energy or time. Then we introduce a novel multichannel deconvolution built upon the 2D-1D MS-VSTS, which allows to get rid of both the noise and the blur introduced by the point spread function (PSF) in each energy (or time) band. The method is applied to simulated data from the Large Area Telescope (LAT), the main instrument of the Fermi Gamma-Ray Space Telescope, which detects high energy gamma-rays in a very wide energy range (from 20 MeV to more than 300 GeV), and whose PSF is strongly energy-dependent (from about 3.5 at 100 MeV to less than 0.1 at 10 GeV)