Implementation of Image Denoising using Thresholding Techniques

Removing noise from the original signal is still a challenging problem for researchers. Wavelet transforms enable us to represent signals with a high degree of scarcity. Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. The aim of this paper was to study various thresholding techniques such as SureShrink, VisuShrink and BayesShrink and determine the best one for image denoising [5].Wavelet denoising attempts to remove the noise present in the signal while preserving the signal characteristics, regardless of its frequency content. It involves three steps: a linear forward wavelet transform, nonlinear thresholding step and a linear inverse wavelet transform.Wavelet thresholding (first proposed by Donoho [1, 2, 3]) is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. It removes noise by killing coefficients that are insignificant relative to some threshold, and turns out to be simple and effective, depends heavily on the choice of a thresholding parameter and the choice of this threshold determines, to a great extent the efficacy of denoising. Index Terms – GGD, MSE, Orthonormal, Thresholding, Wavelet