Wavelet-PDE Based Image Denoising

This project implements an image denoising program based on wavelet-PDE (Partial Differential Equation) methodology, developed using MATLAB 7 programming environment. The program aims to enhance image quality by applying wavelet transform techniques combined with PDE-based algorithms for noise reduction. During implementation, we leverage MATLAB 7's robust computational capabilities and specialized toolboxes, particularly the wavelet toolbox for multi-scale decomposition and custom PDE solvers for diffusion processes. The algorithm typically involves wavelet decomposition to separate noise components from image details, followed by PDE-based diffusion methods that preserve edges while smoothing homogeneous regions. Key functions include wavelet thresholding techniques (soft/hard thresholding) and anisotropic diffusion equations implemented through finite difference methods. Users can efficiently process noisy images through this program, obtaining clearer results with enhanced visual quality through parameter-adjustable denoising operations. We believe this project contributes positively to image processing research and applications, providing users with an effective toolkit for improved image processing experiences through the integration of multi-scale analysis and PDE-based regularization techniques.