Example of Wavelet Denoising: Decomposition and Reconstruction
This example demonstrates the decomposition and reconstruction process for wavelet denoising, including data files and code implementation details.
Semi-Threshold Wavelet Denoising
Semi-threshold wavelet denoising with implementation details, algorithm explanation, and performance comparison against hard-threshold and soft-threshold methods
Wavelet Denoising Method Example for True Color Images
An example of wavelet denoising method for true color images, where RGB channels are processed separately and then recombined to produce the final denoised result. This approach implements channel-wise wavelet thresholding to effectively remove noise while preserving color fidelity.
GUI Application for Image Denoising Experiments
A MATLAB GUI-based application implementing multiple image denoising techniques including wavelet denoising and median filtering, successfully tested and operational.
Multiple Algorithms for Voice Signal Endpoint Detection
Various algorithms for voice signal endpoint detection, including time-domain short-term energy algorithm, short-term zero-crossing rate algorithm, and wavelet denoising procedures with implementation insights.
Donoho Classic Threshold Improvement Method for Wavelet Denoising
Implementation of Donoho Classic Threshold Improvement Method for Wavelet Denoising with MATLAB Source Code Programming
Wavelet Denoising Method Using Multistage Median Filtering
Wavelet denoising approach incorporating multistage median filtering demonstrates superior performance compared to conventional methods, with enhanced noise reduction capabilities and better detail preservation.
MATLAB Implementation of Wavelet Denoising with Local Adaptive Thresholding
This MATLAB-based wavelet denoising approach utilizes inter-scale correlations of wavelet coefficients to address limitations in conventional hard and soft thresholding methods. By introducing a modified compromise method that multiplies the threshold obtained from a double shrinkage function by an appropriate coefficient, we developed a novel locally adaptive denoising algorithm in the wavelet domain. The algorithm effectively removes noise while preserving high-frequency image details through intelligent threshold adjustment and scale-dependent coefficient processing. Experimental results demonstrate superior performance in both noise removal and detail preservation compared to traditional methods.
Time Series Chaos Analysis for Graduation Research
Graduation project focusing on time series chaos analysis, implementing phase space reconstruction, Lyapunov exponent calculation, wavelet denoising techniques with Python/MATLAB code examples
Shape-Based Medical Image Retrieval System
This project presents my undergraduate thesis work on shape-based medical image retrieval, implementing wavelet denoising for preprocessing and utilizing invariant moments as feature descriptors for robust image matching.