Wavelet Denoising Algorithm Implementation in MATLAB

Wavelet denoising is an image processing technique that can be implemented in MATLAB. This method is widely used in graduation thesis projects for optimizing and enhancing images, significantly improving image quality and clarity. The principle of wavelet denoising involves analyzing and transforming images through wavelet decomposition, where noise components are identified and removed using thresholding techniques. The algorithm typically follows these steps: First, apply wavelet transform to decompose the image into different frequency components. Then, implement thresholding methods (soft or hard thresholding) to eliminate noise from the wavelet coefficients. Finally, reconstruct the denoised image using inverse wavelet transform. Key MATLAB functions for implementation include wavedec2 for 2D wavelet decomposition, wthresh for applying thresholds, and waverec2 for image reconstruction. The algorithm effectively removes various types of noise while preserving important image features and edges. Using wavelet denoising technology can substantially enhance visual image quality, making images more attractive and readable. This approach is particularly valuable for medical imaging, remote sensing, and digital photography applications where noise reduction is critical for accurate analysis.