Explore MATLAB source code curated for "小波变换" with clean implementations, documentation, and examples.
Comprehensive MATLAB programs for wavelet transformation including discrete wavelet transform (DWT), multiscale analysis, wavelet packet decomposition, and practical applications in image compression, digital watermarking, and signal denoising with algorithm explanations
Wavelet transform implementation for speech pitch detection, primarily used to identify vocal pitch periods, with comprehensive code description for wavelet decomposition and analysis
The process involves framing and windowing of audio signals, performing 3rd-order wavelet transform on each frame, extracting approximation coefficient averages (typically zero), embedding binary images as watermarks, and implementing blind detection for watermark recovery with code-level implementation insights.
Implementation of image enhancement and segmentation through wavelet transform with comparative analysis against histogram equalization. This approach involves detailed technical comparisons between different segmentation techniques including thresholding, edge detection, and region growing methods.
Practical MATLAB implementation approaches for wavelet transform with code examples and toolbox utilization guidance
MATLAB GUI implementation for digital watermarking using DWT and SVD algorithms with chaotic encryption. This graduation project code combines discrete wavelet transform (DWT) and singular value decomposition (SVD) for watermark embedding and extraction. Features chaotic encryption for enhanced security. After downloading, ensure proper image path configuration in the code.
Wavelet transform-based threshold selection for noise reduction, demonstrating the superior performance of soft thresholding over hard thresholding in signal denoising applications.
Fractal theory utilizing wavelet transforms demonstrates significant reference value in the wavelet fractal domain, with implementations involving signal decomposition and scaling analysis algorithms.
Leveraging the multiscale analysis properties of wavelet transform, this approach improves basic frequency-domain spectral subtraction by applying different thresholds at various wavelet domain scales. It separates voiced and unvoiced sounds during denoising based on their distinct characteristics, preserving unvoiced components to produce fuller-sounding speech while employing adaptive algorithms to further enhance signal quality.
Implementation of power quality detection algorithms utilizing wavelet transform techniques with signal processing and feature extraction capabilities