The new edition of this classic book 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 with Fourier, wavelet and time-frequency transforms, and 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.
Table of Contents
CHAPTER 1 - Sparse Representations
CHAPTER 2 - The Fourier Kingdom
CHAPTER 3 - Discrete Revolution
CHAPTER 4 - Time Meets Frequency
CHAPTER 5 - Frames
CHAPTER 6 - Wavelet Zoom
CHAPTER 7 - Wavelet Bases
CHAPTER 8 - Wavelet Packet and Local Cosine Bases
CHAPTER 9 - Approximations in Bases
CHAPTER 10 - Compression
CHAPTER 11 - Denoising
CHAPTER 12 - Sparsity in Redundant Dictionaries
CHAPTER 13 - Inverse Problems
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