MATLAB Implementation of Wavelet Transform for Image Denoising

This text presents a wavelet transform-based denoising implementation for image processing, which was developed as a key component of my graduation project. Wavelet transform is a powerful signal processing technique that enables effective extraction of meaningful image information while eliminating noise components. In my implementation, the code performs multi-level wavelet decomposition using functions like wavedec2(), applies thresholding techniques (soft or hard thresholding) to wavelet coefficients at different scales, and reconstructs the denoised image using waverec2(). The algorithm effectively separates signal from noise by exploiting the multi-resolution analysis capabilities of wavelet transforms. This program allowed me to deeply understand and apply wavelet transform principles in practical image processing scenarios, significantly improving image quality and clarity through noise reduction. The implementation represents one of the major achievements in my graduation research, contributing valuable insights and practical solutions to image processing applications in related fields.