In medical image processing, image denoising has become a very essential exercise all through the diagnose. Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in MRI images, the noise can restrain information which is valuable for the general practitioner. Consequently medical images are very inconsistent, and it is crucial to operate case to case. The objective of image denoising is to reduce the noise while retaining the fine details of an image. This paper presents a Wavelet based scheme for noise detection & removal in MRI images. The motivation to use wavelet as a possible alternative is to explore new ways to reduce computational complexity and to achieve better noise reduction performance. The entire set of wavelet share some common properties but each wavelet has certain unique properties of image decomposition, denoising and reconstruction which provides difference in PSNR and MSE. In this paper, Quantitative and qualitative comparisons of the results obtained by the daubechies wavelet transform and mallat wavelet transform for the salt & pepper noise and Gaussian noise. It shows that mallat transform using soft thresholding demonstrate its higher performance for salt and paper reduction & Gaussian noise reduction.
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