Applications of Wavelets in Signal Processing
Wavelet applications in signal processing, including signal decomposition and reconstruction using wavelet transforms, along with noise thresholding techniques for signal enhancement.
Wavelet and Multiwavelet Programs with Code Implementation
Comprehensive guide to wavelet and multiwavelet programs with detailed explanations and MATLAB/Python code examples - ideal for beginners starting with multiwavelets
Wavelet-Based Digital Watermarking Algorithm
A wavelet-based digital watermarking algorithm implemented using MATLAB simulation, featuring discrete wavelet transform (DWT) operations and watermark embedding/extraction techniques.
Image Segmentation Using Ridgelet and Wavelet Transforms
This program implements image segmentation based on ridgelet and wavelet transforms, including sample images for algorithm demonstration and validation
Audio Compression Using Wavelet Transform with Parameter Comparison
This program implements audio compression through wavelet transforms, enabling comparison of different wavelet functions and compression levels to analyze their impact on audio quality
Wavelet-Based Feature Extraction for Processed Images
A MATLAB program for performing wavelet-based feature extraction on processed images, featuring efficient implementation and user-friendly interface!
Image Denoising Using Wavelet and Fractal Methods with Comparative Analysis
This study implements image denoising using wavelet and fractal methodologies, followed by a comprehensive comparison of their performance. The implementation includes practical code considerations for both approaches, evaluating noise reduction efficiency and detail preservation capabilities.
Wavelet Soft-Threshold Denoising Implementation
Reliable source code for wavelet soft-threshold denoising algorithm with complete signal processing pipeline. Features multi-level decomposition, adaptive threshold calculation, and perfect reconstruction capabilities.
Multiscale Edge Detection Using Wavelets
Implementing multiscale edge detection using wavelet analysis, including Canny algorithm integration and comprehensive multiscale edge detection program with code implementation details
Compressive Sensing: Wavelet-Based Sparsification with OMP Algorithm for Reconstruction and Recovery
Compressive sensing is an emerging and critically important discipline. This resource presents the most classic and straightforward framework from Hong Kong University's Sha Wei, implementing wavelet-based sparsification followed by Orthogonal Matching Pursuit (OMP) algorithm for signal reconstruction and recovery.