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Denoising of Medical Images Using Multiwavelet Transforms and Various Thresholding Techniques

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Author(s): Abdullah Al Jumah | Mohammed Gulam Ahamad | Syed Amjad Ali

Journal: Journal of Signal and Information Processing
ISSN 2159-4465

Volume: 04;
Issue: 01;
Start page: 24;
Date: 2013;
Original page

Keywords: Multiwavelets | Noise | Thresholding | Additive White Gaussian Noise | Signal-to-Noise Ratio | Discrete Multiwavelet Transforms | Chui Lian | Symmetric Asymmetric Multiwavelet Transform | Bi-Hermite Multiwavelet Transform | Modified Universal Thresholding

ABSTRACT
The problem of estimating an image corrupted by additive white Gaussian noise has been of interest for practical reasons. Non-linear denoising methods based on wavelets, have become popular but Multiwavelets outperform wavelets in image denoising. Multiwavelets are wavelets with several scaling and wavelet functions, offer simultaneously Orthogonality, Symmetry, Short support and Vanishing moments, which is not possible with ordinary (scalar) wavelets. These properties make Multiwavelets promising for image processing applications, such as image denoising. The aim of this paper is to apply various non-linear thresholding techniques such as hard, soft, universal, modified universal, fixed and multivariate thresholding in Multiwavelet transform domain such as Discrete Multiwavelet Transform, Symmetric Asymmetric (SA4), Chui Lian (CL), and Bi-Hermite (Bih52S) for different Multiwavelets at different levels, to denoise an image and determine the best one out of it. The performance of denoising algorithms and various thresholding are measured using quantitative performance measures such as, Mean Square Error (MSE), and Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR). It is found that CL Multiwavelet transform in combination with modified universal thresholding has given best results.
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