Damiana Lazzaro

Radial basis functions for the multivariate interpolation of large scattered data sets

An efficient method for the multivariate interpolation of very large scattered data sets is presented. It is based on the local use of radial basis functions and represents a further improvement of the well known Shepards method. Although the latter is simple and well suited for multivariate interpolation, it does not share the good reproduction quality of other methods widely used for bivariate interpolation. On the other band, radial basis functions, which have proven to be highly useful for multivariate scattered data interpolation, have a severe drawback. They are unable to interpolate large sets in an efficient and numerically stable way and maintain a good level of reproduction quality at the same time. Both problems have been circumvented using radial basis functions to evaluate the nodal function of the modified Shepards method. This approach exploits the flexibility of the method and improves its reproduction quality. The proposed algorithm has been implemented and numerical results confirm its efficiency.

Image compression through embedded multiwavelet transform coding

In this paper, multiwavelets are considered in the context of image compression and two orthonormal multiwavelet bases are experimented, each used in connection with its proper prefilter. For evaluating the effectiveness of multiwavelet transform for coding images at low bit-rates, an efficient embedded coding of multiwavelet coefficients has been realized. The performance of this multiwavelet-based coder is compared with the results obtained for scalar wavelets.

Shape preserving surface reconstruction using locally anisotropic radial basis function interpolants

In this paper we deal with the problem of reconstructing surfaces from unorganized sets of points, while capturing the significant geometry details of the modelled surface, such as edges, flat regions, and corners. This is obtained by exploiting the good approximation capabilities of the radial basis functions (RBF), the local nature of the method proposed in [1], and introducing information on shape features and data anisotropies detected from the given surface points. The result is a shape-preserving reconstruction, given by a weighted combination of locally aniso tropic interpolants. For each local interpolant the anisotropy is obtained by replacing the Euclidean norm with a suitable metric which takes into account the local distribution of the points. Thus hyperellipsoid basis functions, named anisotropic RBFs, are defined. Results from the application of the method to the reconstruction of object surfaces in @?^3 are presented, confirming the effectiveness of the approach.

Fast surface reconstruction and hole filling using positive definite radial basis functions

AbstractnSurface reconstruction from large unorganized data sets is very challenging, especially if the data present undesired holes. This is usually the case when the data come from laser scanner 3D acquisitions or if they represent damaged objects to be restored. An attractive field of research focuses on situations in which these holes are too geometrically and topologically complex to fill using triangulation algorithms. In this work a local approach to surface reconstruction from point-clouds based on positive definite Radial Basis Functions (RBF) is presented that progressively fills the holes by expanding the neighbouring information. The method is based on the algorithm introduced in [7] which has been successfully tested for the smooth multivariate interpolation of large scattered data sets. The local nature of the algorithm allows for real time handling of large amounts of data, since the computation is limited to suitable small areas, thus avoiding the critical efficiency problem involved in RBF multivariate interpolation. Several tests on simulated and real data sets demonstrate the efficiency and the quality of the reconstructions obtained using the proposed algorithm.n

An algebraic construction of k-balanced multiwavelets via the lifting scheme

Multiwavelets have been revealed to be a successful generalization within the context of wavelet theory. Recently Lebrun and Vetterli have introduced the concept of “balanced” multiwavelets, which present properties that are usually absent in the case of classical multiwavelets and do not need the prefiltering step. In this work we present an algebraic construction of biorthogonal multiwavelets by means of the well-known “lifting scheme”. The flexibility of this tool allows us to exploit the degrees of freedom left after satisfying the perfect reconstruction condition in order to obtain finite k-balanced multifilters with custom-designed properties which give rise to new balanced multiwavelet bases. All the problems we deal with are stated in the framework of banded block recursive matrices, since simplified algebraic conditions can be derived from this recursive approach.

An Iterative

Regularization methods for the solution of ill-posed inverse problems can be successfully applied if a right estimation of the regularization parameter is known. In this paper, we consider the L1-regularized image deblurring problem and evaluate its solution using the iterative forward-backward splitting method. Based on this approach, we propose a new adaptive rule for the estimation of the regularization parameter that, at each iteration, dynamically updates the parameter value, following the evolution of the objective functional. The iterative algorithm automatically stops, without requiring any assumption about the perturbation process, when the parameter has reached a seemingly near optimal value. In spite of the fact that the optimality of this value has not yet been theoretically proved, a large number of numerical experiments confirm that the proposed rule yields restoration results competitive with those of the best state-of-the-art algorithms.

L_{1}

The lifting scheme has been proposed as a new idea for the construction of 2-band compactly supported wavelets with compactly-supported duals. The basic idea behind the lifting scheme is that it provides a simple relationship between all multiresolution analyses sharing the same scaling function. It is therefore possible to obtain custom-designed compactly supported wavelets with required regularity, vanishing moments, shape, etc. In this work, we generalize the lifting scheme for the construction of compactly-supported biorthogonal M-band filters. As in the previous case, we used the flexibility of the scheme to exploit the degree of freedom left after satisfying the perfect-reconstruction conditions in order to obtain finite filters with some interesting properties, such as vanishing moments, symmetry, shape, etc., or that satisfy certain optimality requests required for particular applications. Moreover, for these lifted biorthogonal M-band filters, we give an analysis-synthesis algorithm which is more efficient than the standard algorithm realized with filters with similar compression capabilities.

-Based Image Restoration Algorithm With an Adaptive Parameter Estimation

This paper presents an efficient and highly scalable parallel version of the Modified RBF Shepards method presented in [5]. This method maintains the metric nature and the advantages of Shepards method and, at the same time, improves its accuracy by exploiting the characteristics of flexibility and accuracy which have made the radial basis functions a well-established tool for multivariate interpolation. Due to its locality, this method can be easily and efficiently parallelized on a distributed memory parallel architecture. The performance of the parallel algorithm has been studied theoretically and the experimental results obtained by running its implementation on a Cray T3E parallel machine, using the MPI interface, confirm the theoretical efficiency.

Biorthogonal M-band filter construction using the lifting scheme

Abstract The theory of block recursive matrices has been revealed to be a flexible tool in order to easily prove some properties concerning the classical theory of multiwavelet functions. Multiwavelets are a recent generalization of scalar wavelets, and their principal advantage, compared to scalar wavelets, is that they allow us to work with a higher number of degrees of freedom. In this work, we present some applications of the block recursive matrix theory to the solution of some practical problems. More precisely, we will show that the possibility of explicitly describing the product of particular block recursive matrices and of their transposes allows us to solve the problems o fthe construction and evaluation of multiwavelet functions quite simply.

A parallel multivariate interpolation algorithm with radial basis functions

The eigenfunctions of the Laplace–Beltrami operator (manifold harmonics) define a function basis that can be used in spectral analysis on manifolds. Inxa0Ozoli et al. (Proc Nat Acad Sci 110(46):18368–18373, 2013) the authors recast the problem as an orthogonality constrained optimization problem and pioneer the use of an