A Wavelet Tour of Signal Processing: The Sparse Way, 3/e (Hardcover)

<本書特色>

1 .  Sparse signal representations in dictionaries
2 .  Compressive sensing, super-resolution and source separation.
3 .  Geometric image processing with curvelets and bandlets
4 .  Wavelets for computer graphics with lifting on surfaces
5 .  Time-frequency audio processing and denoising
6 .  Image compression with JPEG-2000
7 .  New exercises
8 .  Balances presentation of the mathematics with applications to signal processing
9 .  Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox
10 .  Companion website for instructors and selected solutions and code available for students

<內容簡介>

The new edition of this classic gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today’s signal processing. The book clearly presents the standard representations of Fourier, wavelet and time-frequency tools which enable sparse representations of large classes of signals and images, including the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.

<章節目錄>

Preface to the Sparse Edition Notations
1. Sparse Representations
2. Fourier Kingdom
3. Discrete Revolution
4. Time Meets Frequency
5. Frames
6. Wavelet Zoom
7. Wavelet Bases
8. Wavelet Packet and Local Cosine Bases
9. Approximations in Bases
10. Compression
11. Denoising
12. Sparse in Redundant Dictionaries
A. Mathematical Complements
A.1 Functions and Integration
A.2 Banach and Hilbert Spaces
A.3 Bases of Hilbert Spaces
A.4 Linear Operators
A.5 Separable Spaces and Bases
A.6 Random Vectors and Covariance Operators
A.7 Diracs