Applied Wavelet Analysis

Applied Wavelet Analysis

Denoising, Trend Extraction, and Compression

Volume II-A focuses on the applied use of discrete wavelet transforms for practical signal and image processing tasks. Building on the theoretical foundations established in Volume I, this volume emphasizes denoising, compression, and trend analysis for one-dimensional signals and two-dimensional image data. The presentation prioritizes method selection, parameter tuning, and interpretability, enabling readers to move from theoretical understanding to effective application.

Throughout the volume, wavelet methods are developed within reproducible Python workflows and evaluated using established quantitative metrics. Comparisons with classical filtering and compression approaches are included where appropriate, clarifying the advantages and limitations of wavelet-based techniques in real analytical settings. Together, these chapters provide a structured and application-oriented guide to using wavelets as practical tools for signal and image analysis.

This volume covers:

• Discrete wavelet–based denoising of time-series and sensor signals
• Signal and speech compression using multiresolution representations
• Trend extraction and anomaly detection in nonstationary data
• Image denoising and image compression with wavelet transforms
• Quantitative evaluation using established error and quality metrics
• Reproducible Python workflows for applied wavelet analysis