Wavelet Transform-Based Image Enhancement and Denoising Implemented in MATLAB

This document provides detailed technical insights into wavelet transform-based image enhancement and denoising. Wavelet transform is a mathematical approach that decomposes signals or images into different frequency components, enabling effective extraction of meaningful information and noise suppression. In MATLAB implementation, key functions like wavedec2 (for 2D wavelet decomposition) and waverec2 (for reconstruction) form the core processing pipeline. The algorithm typically involves three main steps: 1) Decomposition: Using wavelet families (e.g., Daubechies, Symlets) to break down the image into approximation and detail coefficients 2) Thresholding: Applying soft/hard thresholding techniques (via wthresh function) to suppress noise in detail coefficients 3) Reconstruction: Combining processed coefficients to generate enhanced images Through careful selection of wavelet filters and threshold parameters, we can adjust image contrast/brightness while reducing Gaussian, salt-and-pepper, or multiplicative noise. The implementation often incorporates histogram equalization and contrast stretching techniques for optimal enhancement. This methodology finds extensive applications in medical imaging (e.g., MRI enhancement), non-destructive testing, and computer vision systems. The wavelet-based approach proves particularly effective in preserving edges and textures while removing artifacts, making it a robust solution for improving image quality and visual perception in technical applications.