Explore MATLAB source code curated for "小波变换" with clean implementations, documentation, and examples.
This method primarily utilizes wavelet transform for image compression and evaluates compression performance by calculating key metrics including compression ratio and signal-to-noise ratio (SNR) of the compressed image.
Self-developed implementation of quadratic and cubic B-splines suitable for wavelet transformations and curve fitting applications. This code provides a foundational framework for beginners to study, modify, and extend with additional features for enhanced functionality.
Application of wavelet transform for motor fault signal analysis, covering fundamental knowledge in motor fault diagnosis and condition monitoring including detection methods, allowable limits, diagnostic equipment principles, along with Hilbert transform-based signal analysis implementation approaches.
This suite of programs implements signal singularity detection and denoising routines using wavelet transforms. Each program's specific functionality, including implementation approaches for multi-scale analysis and thresholding techniques, is detailed in the source code.
Implementation of accurate start/end time detection for voltage sags using wavelet transform and modulus maximum principles, featuring signal processing and edge detection algorithms
MATLAB programs based on wavelet transform applications, featuring image enhancement, image decomposition, image denoising, and image fusion with comprehensive code implementation details
Image Enhancement Using Wavelet Transform: A Non-Subsampled Wavelet Decomposition Approach for Processing High-Frequency Coefficients and Reconstruction
A MATLAB program utilizing wavelet transforms for speech signal denoising through threshold-based noise removal, featuring parameter optimization capabilities
Comparison of wavelet transform and OMP algorithm in compressed sensing, featuring a relatively new algorithmic approach with implementation-focused analysis
Feature extraction from fault signals using wavelet transform with feature vector construction methodology