摘要：Due to sparsity and multiresolution properties, Mutiscale transforms aregaining popularity in the field of medical image denoising. This paper empiricallyevaluates different Mutiscale transform approaches such as Wavelet, Bandelet,Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoisedfirst undergoes decomposition and then the thresholding is applied to its coefficients.This paper also deals with basic shrinkage thresholding techniques such Visushrink,Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink todetermine the best one for image denoising. Experimental results on several testimages were taken on Magnetic Resonance Imaging (MRI), X-RAY and ComputedTomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio(PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM),and Correlation Coefficient (CC) were computed. The results shows that Contourletbased Medical image denoising methods are achieving significant improvement inassociation with Neighsureshrink thresholding technique.
