Over the last few years, the network community has started to rely heavily on the use of novel concepts such as self-similarity and Long-Range Dependence (LRD). Despite their wide use, there is still much confusion regarding ...

3D surfaces are used in applications such as animations, 3D object modeling and visualization. The geometries of such surfaces are often approximated using polygonal meshes. This thesis aims to compress 3D geometry meshes ...

This paper describes the development and use of multiscale, platelet-based image reconstruction algorithms in medical imaging. Such algorithms are effective because platelets approximate images in certain (piecewise) ...

Accurate prediction of wavelet coefficients relies on an understanding of the phase effects of edge alignment. This research examines techniques for uncovering edge information based on the available coefficients. These ...

This paper describes a statistical modeling and analysis method for linear inverse problems involving Poisson data based on a novel multiscale framework. The framework itself is founded upon a multiscale analysis associated ...

This paper introduces an <i>expectation-maximization</i> (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a ...

Wavelet domain algorithms have risen to the forefront of image processing. The power of these algorithms is derived from the fact that the wavelet transform restructures the image in a way that makes statistical modeling ...

In this paper, we demonstrate based on the linear model of Kite that inverse halftoning is equivalent to the well-studied problem of deconvolution in the presence of colored noise. We propose the use of the simple and ...

Wavelet-based image coders optimally represent smooth regions and isolated point singularities. However, wavelet coders are less adept at representing perceptually important edge singularities, and coding performance ...

We propose an efficient, hybrid <i>Fourier-Wavelet Regularized Deconvolution</i> (ForWaRD) algorithm that performs noise regularization via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage ...