Journal
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
Volume 11, Issue 1, Pages 89-123Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1006/acha.2000.0350
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We develop a new class of non-Gaussian multiscale stochastic processes defined by random cascades on trees of multiresolution coefficients, These cascades reproduce a semiparametric class of random variables known as Gaussian scale mixtures, members of which include many of the best known, heavy-tailed distributions. This class of cascade models is rich enough to accurately capture the remarkably regular and non-Gaussian features of natural images, but also sufficiently structured to permit the development of efficient algorithms. In particular, we develop an efficient technique for estimation, and demonstrate in a denoising application that it preserves natural image structure (e.g,, edges). Our framework generates global yet structured image models, thereby providing a unified basis for a variety of applications in signal and image processing, including image denoising, coding, acid super-resolution. (C) 2001 Acadrmic Press
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