Nonlinear signal and image processing methods are fast emerging as an alternative to established linear methods for meeting the challenges of increasingly sophisticated applications. Advances in computing performance and nonlinear theory are making nonlinear techniques not only viable, but practical.

This book details recent advances in nonlinear theory and methods and explores an array of modern signal and image processing applications. The first several chapters focus on nonlinear signal processing theory, targeting three critical areas: filter analysis, nonlinear filter class design, and signal analysis. The remaining chapters explore nonlinear approaches across the broad spectrum of applications with signal processing components, from data traffic modeling and image enhancement to cutting edge applications in genomics.

All of the chapters were contributed by well-known theorists and application-driven researchers who explore current and emerging nonlinear methods from their theoretical background and practical algorithms through the potential of these methods for solving important open questions. Nonlinear Signal and Image Processing: Theory, Methods, and Applications thus provides a singular opportunity to build a strong, fundamental understanding of nonlinear theory and methods and a foundation upon which to approach many of today's most interesting and challenging signal processing problems.