"A Primer on Wavelets and Their Scientific Applications" by James S. Walker (Repost)

Updated and fully revised to include the latest developments, this second edition of A Primer on Wavelets and Their Scientific Applications guides readers through the main ideas of wavelet analysis in order to develop a thorough appreciation of wavelet applications.

Ingeniously relying on elementary algebra and just a smidgen of calculus, Professor Walker demonstrates how the underlying ideas behind wavelet analysis can be applied to solve significant problems in audio and image processing, as well in biology and medicine.

In Appendix there are
• 104 worked examples and 222 exercises, constituting a veritable book of review material; and
• software that permits recording, playing, and modifying sound files using wavelet techniques, plus a facility for displaying, printing, and modifying standard IEEE gray field images; Include Biorthogonal Wavelets, The JPEG200 Image Compression Standard, Spectrograms, Analyzing Speech and Music with Spectrograms, Wavelet-Based Denoising of Images, and Thresholding Spectrograms for Denoising.

Contents
Preface
Chapter 1: Overview
1.1 What is wavelet analysis?
1.2 Notes and references
Chapter 2: Haar wavelets
2.1 The Haar transform
2.2 Conservation and compaction of energy
2.3 Haar wavelets
2.4 Multiresolution analysis
2.5 Signal compression
2.6 Removing noise
2.7 Notes and references
2.8 Examples and exercises
Chapter 3: Daubechies wavelets
3.1 The Daub4 wavelets
3.2 Conservation and compaction of energy
3.3 Other Daubechies wavelets
3.4 Compression of audio signals
3.5 Quantization, entropy, and compression
3.6 Denoising audio signals
3.7 Biorthogonal wavelets
3.8 The Daub 9/7 system
3.9 Notes and references
3.10 Examples and exercises
Chapter 4: Two-dimensional wavelets
4.1 Two-dimensional wavelet transforms
4.2 Compression of images—fundamentals
4.3 Fingerprint compression
4.4 The WDR algorithm
4.5 The ASWDR algorithm
4.6 Important image compression features
4.7 JPEG 2000 image compression
4.8 Denoising images
4.9 Some topics in image processing
4.10 Notes and references
4.11 Examples and exercises
Chapter 5: Frequency analysis
5.1 Discrete Fourier analysis
5.2 Definition of the DFT and its properties
5.3 Frequency description of wavelet analysis
5.4 Correlation and feature detection
5.5 Object detection in 2D images
5.6 Creating scaling signals and wavelets
5.7 Gabor transforms and spectrograms
5.8 Musical analysis
5.9 Inverting Gabor transforms
5.10 Gabor transforms and denoising
5.11 Notes and references
5.12 Examples and exercises
Chapter 6: Beyond wavelets
6.1 Wavelet packet transforms
6.2 Wavelet packet transforms—applications
6.3 Continuous wavelet transforms
6.4 Gabor wavelets and speech analysis
6.5 Percussion scalograms and musical rhythm
6.6 Notes and references
6.7 Examples and exercises
Appendix A: Projects
A.l Music
A.2 Noise removal from audio
A.3 Wavelet image processing
A.4 References
Appendix B: Selected exercise solutions
B.l Introduction
B.2 Chapter 2
B.3 Chapter 3
B.4 Chapter 4
B.5 Chapter 5
B.6 Chapter 6
Appendix C: Wavelet software
C.1 Installing the book's software
C.2 Other software
C.3 References
Bibliography