Sparse Image and Signal Processing
Wavelets and Related Geometric Multiscale Analysis, Second Edition

Presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, MRI, media, and forensics.

Jean-Luc Starck (Author), Fionn Murtagh (Author), Jalal Fadili (Author)

9781107088061, Cambridge University Press

Hardback, published 14 October 2015

428 pages, 194 b/w illus. 109 colour illus. 8 tables
26.1 x 18.6 x 2.8 cm, 1 kg

Review of previous edition: 'A welcome addition to the image processing library.' T. Kubota, Computing Reviews

This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.

1. Introduction to the world of sparsity
2. The wavelet transform
3. Redundant wavelet transform
4. Nonlinear multiscale transforms
5. Multiscale geometric transforms
6. Sparsity and noise removal
7. Linear inverse problems
8. Morphological diversity
9. Sparse blind source separation
10. Dictionary learning
11. Three-dimensional sparse representations
12. Multiscale geometric analysis on the sphere
13. Compressed sensing
14. This book's take-home message.

Subject Areas: Image processing [UYT], Signal processing [UYS], Mathematical theory of computation [UYA], Computer science [UY], Astrophysics [PHVB]