"Wavelet Based Image Denoising Implementation"

Abstract

Magnetic Resonance Imaging (MRI) is a powerful diagnostic technique. However, the incorporated noise during image acquisition degrades the human interpretation, or computer-aided analysis of the images. Time averaging of image sequences aimed at improving the signal-to-noise ratio (SNR) would result in additional acquisition time and reduce the temporal resolution. Therefore, denoising should be performed to improve the image quality for more accurate diagnosis.This project implements different approaches of wavelet based image denoising methods. The wavelet transform is a simple and elegant tool that can be used for many digital signal processing applications. It overcomes some of the limitations of the Fourier transform with its ability to represent a function simultaneously in the frequency and time domains using a single prototype function (or wavelet) and its scales and shifts. In this project, some emerging wavelet domain Denoising methods such as soft and hard thresholding, bayeshrink, visushrink and SUREshrink are considered. The basic idea behind this thesis is the estimation of the uncorrupted image from the distorted or noisy image, and is referred to as image "denoising".