| An image is often corrupted by noise (refers to additive white Gaussian noise in this paper) in its acquisition. The goal of denoising is to remove the noise while retaining as much as possible the important signal features. So, in order to increase PSNR and definition of the constructed images, image denoising and edge information maintained are first considered. According to this, idea of image denoising of this paper is presented: image thresholding denoising based on edge detection and Bayesian estimation. The method put forward is depended on these: (1)Traditionally, the approaches of denoising are achieved by linear processing such as Wiener filtering, which can restrain the high-frequency parts and decrease noise but may destroy image details. They have many defects to unstatic signals. (2)Nonlinear techniques are used in image denoising. After wavelet decomposing, most of the energy of the original image is concentrated in the low-frequency subband, while noise is distributed in high-frequency components. Edge information is high-frequency information, so traditional thresholding shrinkage will throw off the useful edge information as cutting off those high-frequency noises, which blur sharp edges. Those wavelet coefficients of an image that are corresponding to image's edges are first detected by the method of wavelet edge detection. The detected wavelet coefficients will be protected from denoising. (3)It is important to choose thresholding. Global thresholding can throw off the important information of image, while local thresholding with locality is calculated by local components, which provided better performance on denoising. Ideal thresholding from thresholding Eq. is achieved just depended on Bayesian risk estimation. There are two stages of the procedure which operates as follows: Wavelet edge detection is in first stage. (1) Smooth A part of the noise is cut off through smoothing the noisy image, which may reduce the calculation as edge detection. The details of the image are misty ,which was created by linear filter , while it can be overcame by median filter. At the same time, median filter is adapt to cut off the isolated noise. Median filter which size of the window is 3×3 is adopted in this paper. (2)proceeding the image by wavelet edge detection According to the method of the wavelet edge detection expressed in chapter 4, image by pre-proceeded is detected through multi-scale wavelet edge detection. The locations of edge are confirmed, and the coefficients of the edge are maintained, while others are proceeded through the wavelet thresholding. The thresholding procedure is in second stage. (1)denoising image of the wavelet transform Denoising image is decomposed through two-dimensional WT to obtain low-frequency subband and detail subbands. The energy of the noise reduces 90% when the scale is 3. (2)Every threshold of denoising of detail subbands is computed, according to T? = σ? 2σ?X based on Bayesian estimation. According to Eq. (5.18-5.22), every parameter is calculated. (3) wavelet-thresholding denosing processing To get rid of the noise in the detail subbands, wavelet coefficients of non-edge are processed by Eq. obtained from (2). While wavelet coefficients of edge are not processed by thresholding. According to this, wavelet coefficients are obtained. According to the comparison between hard-thresholding and soft-thresholding, soft thresholding is applied in this paper. ??ω? j , k= ??? 0si gn (ωj,k)(ωj,k?T?)ωωjj ,,kk≥ |
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