A Review of Different Image Denoising Methods

Removing noise from the original signal is still a challenging problem for researchers. 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 Ultrasound 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 wavelet transform is a simple and elegant tool that can be used for many digital image processing applications. It overcomes some of the limitations of the fourier transform with its ability to represent a function simultaneously in the frequency and time domains using a single prototype function (or wavelet) and its scales and shifts. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper presents a review of some significant work in the area of image denoising.