A WAVELET APPROACH FOR MEDICAL IMAGE DENOISING
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Abstract
Medical Images have always been vulnerable to high level components of noises. Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography and Ultrasound are among most popular techniques for producing medical images, during image capture and transmission noise is added in the images that decreases the image quality and leads to poor image analysis. Various denoising techniques are used to remove the noise or distortion from images while preserving the original quality of the image among which wavelet transform has been proved an efficient one in reducing the noise level. The aim of this paper to characterize the Gaussian noise in wavelet transforms subsequently a threshold based denoising algorithm has been developed using hard and soft thresholding techniques that works on Haar, Daubechies and Symlet Transforms. Firstly the image is decomposed using Haar and Daubechies and symlet transforms, and then the level of soft and hard threshold is selected for reducing the noise in the image and finally the comparison between them has been done on the basis of calculated PSNR& MSE of an image for every wavelet.
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