Image Adaptive Threshold Denoising Based On Edge Enhancement

With multi-scale characterization,wavelet analysis was widely used to multi-scale edge detection. Furthermore, an algorithm of detecting the edge of the noising image through the maximum value of wavelet transform module was put forward,with which"good edges"will be obtained while the edge positions will be kept well. After wavelet decomposing,most of the energy of the original image is concentrated in the low- frequency band , while noise is distributed in high-frequency components. Edge information is the most useful high-frequency information. Therefore, using traditional denoising methods can remove the noise of the image, but they can not retain the edge information. In addition Global thresholding can throw off the important information of image, so the image information is removed.This article proposes a method of image denoising based on adaptive soft-threshold and edge enhancement. To enhance edge, first we use the edge detection to detect the edge of the image, then we preprocess the edge image, next we can use wavelet transform to process the processed edge image and noise image, then we add these wavelet coefficients at the corresponding site, so we can use the soft threshold denoising technology. Since the edge is enhanced before soft threshold, so the edge can be kept. From the result we can see that comparing the traditional denoising methods , this denoising method has good denoising image and the PSNR is higher.