MATLAB Implementation of Image Denoising Using Wavelet Transform

Wavelet-based image denoising programs serve as highly effective tools for processing blurred image files. By eliminating noise and improving resolution, these programs enhance image clarity and detail preservation. This implementation utilizes wavelet transform technology to analyze and process images through multi-scale decomposition, effectively removing noise while maintaining critical image features. The core algorithm typically involves: 1) Applying discrete wavelet transform (DWT) using functions like wavedec2 for 2D decomposition, 2) Noise thresholding through methods such as BayesShrink or SureShrink applied via wthresh, and 3) Reconstruction using inverse wavelet transform (waverec2). For both individual users and professionals, this wavelet denoising program represents a practical solution that plays a significant role in digital image processing workflows, particularly in medical imaging and photographic enhancement applications where detail preservation is crucial.