A Wavelet Transform-Based Image Denoising Program with MATLAB Source Code

This is a MATLAB source code implementation of an image denoising program based on wavelet transform. The program effectively removes noise from images, significantly improving image quality and clarity. Wavelet transform serves as a powerful mathematical tool that analyzes image frequency and amplitude characteristics to separate noise components from genuine signals through multi-resolution analysis. The implementation leverages MATLAB's comprehensive image processing toolbox, utilizing key functions such as wavedec2 for 2D wavelet decomposition and waverec2 for wavelet reconstruction. The algorithm typically involves three main steps: wavelet decomposition of the noisy image, threshold application to wavelet coefficients (using either soft or hard thresholding methods), and inverse wavelet reconstruction to generate the denoised image. The code incorporates optimal wavelet basis selection (such as Daubechies or Symlets wavelets) and adaptive threshold determination strategies to achieve balanced noise removal while preserving important image features and edges. The program demonstrates efficient handling of various noise types including Gaussian noise and salt-and-pepper noise through customizable parameter settings. This implementation provides an excellent solution for researchers and engineers requiring robust image denoising capabilities, featuring modular code structure that allows easy customization of wavelet types, decomposition levels, and thresholding parameters according to specific application requirements.