Providing a novel approach to sparsity

This comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis.

Key themes

Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour — Gaussian and sparse — and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis.

Focus of the core content

The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics.

Practical applications

The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction.

Intended audience

This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.