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Wavelet Thresholding Approach for Image Denoising

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Executive Summary

The original image corrupted by Gaussian noise is a long established problem in signal or image processing .This noise is removed by using wavelet thresholding by focused on statistical modelling of wavelet coefficients and the optimal choice of thresholds called as image denoising. For the first part, threshold is driven in a Bayesian technique to use probabilistic model of the image wavelet coefficients that are dependent on the higher order moments of Generalized Gaussian Distribution (GGD) in image processing applications. The second part of the paper is attempt to claim on lossy compression can be used for image denoising. Thus achieving the image compression & image denoising simultaneously.

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