A REVIEW WAVELET THRESHOLDING APPROACH FOR NOISE REDUCTION IN DIGITAL IMAGE PROCESSING

Computer science & Engineering

The original image corrupted by Gaussian noise is a long established problem in image processing. It is statistical type of noise. It is based on Probability density function and this probability density function is always equal to normal distribution. There are specific types of filters used for Gaussian noise reduction such as spatial filter, in an image when smoothing occurs undesirable results may occur in the blurring of fine-scaled image edges and details due to the blockage of high frequencies. In this paper the necessary idea of an uncorrected image from the noise image identified by “noise reduction method” choosing the excellent way play an important role for getting the desired image. In this review paper, This noise is removed by using wavelet thresholding by focusing on statistical modelling of wavelet coefficients and the optimal choice of thresholds called as image de-noising. threshold is driven in a Bayesian technique a denoising method is presented for noise reduction in Ultrasound images. This paper proposes an efficient method based on linear filtering for images which is corrupted due to Gaussian noise. The method consists in linear filtering of proper wavelet coefficients of the image, corresponding to diagonal and vertical details. The proposed denoising method has good performance and the effectiveness of the proposed method is well demonstrated by experiments on both standard image and real images.

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