Implementation of Various Wavelet-Based Denoising Techniques

This document presents my recently completed image processing assignment where I conducted in-depth research on various wavelet-based denoising methodologies. The implementation includes detailed MATLAB code for hard thresholding and soft thresholding techniques, which effectively remove noise while preserving image edges through wavelet coefficient manipulation. The hard thresholding function zeros coefficients below a specified threshold, while soft thresholding applies smooth shrinkage to wavelet coefficients using mathematical operations like wthresh() and wavedec2(). Additionally, I explored several related topics including image enhancement algorithms that improve visual quality through histogram equalization and contrast adjustment, and image reconstruction techniques that restore degraded images using inverse wavelet transforms via waverec2(). These implementations demonstrate practical applications of wavelet transforms using MATLAB's Wavelet Toolbox functions such as dwt2() and idwt2() for decomposition and reconstruction. The knowledge and technical skills developed through this work hold significant importance for future research and applications in the image processing field. I believe this documentation, complete with executable code examples and algorithm explanations, will prove highly beneficial for students and professionals engaged in digital image processing studies or projects.