Wavelet Threshold Denoising Implementation in MATLAB with SNR Analysis

This article explores the process of wavelet threshold denoising, a widely-used image processing technique for removing noise from images. To demonstrate the effectiveness of this method, we implement a MATLAB workflow that first adds specified noise to an input image, then applies wavelet threshold denoising using 1 to 4 levels of decomposition. The implementation involves key functions such as wavedec2 for multi-level wavelet decomposition and wthresh for applying soft/hard thresholding to wavelet coefficients. The code calculates and displays signal-to-noise ratio (SNR) trend graphs across different decomposition levels, providing quantitative evaluation of denoising performance. This visualization helps in understanding how denoising effectiveness varies with decomposition depth and threshold selection strategies. Additionally, we discuss practical applications of wavelet threshold denoising in various image processing scenarios and address common misconceptions about parameter selection and performance limitations. The MATLAB implementation includes optimization techniques for threshold calculation using universal threshold rules or level-dependent threshold adjustments, ensuring adaptive noise removal while preserving important image features.