Wavelet Image Denoising Based On Edge Etection

Wavelet analysis is a tool of time-frequency analysis after Fourier analysis. In the field of image processing, its application covered imaging technique, image pre-processing, image compression and transferring, image registration, image analysis, feature extraction and pattern classification, etc. In this paper, it's researched on wavelets application in the fields of edge detection, image denoising. .The traditional methods of edge detection are based on one-order derivative's maximum, or two-order derivative's zero-crossing. This kind of edge definition is very sensitive to noises. And thus, edge detection should be carried out in large scale, by which the image was smoothed. One of the shortcomings of edge detection in large scale is that it's difficult to locate edge precisely, which will make mistakes in pattern recognition based on edge features. 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 high-low double thresholds and 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 suband, 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.Because of the shortcomings of traditional denoising methods which can not retain the edge features and global thresholding throwing off the important information of image, the paper firstly detects the edge of the image. After analysing the soft threshold function, the smoothness threshold function is proposed by modifying the soft threshold function.because this function keeps smooth in the entire value range, some important details in the process of image denoising can be held. Firstly we detect the edge of the noising image. Thus we can derive marginal image in order to protect edge information priorly.Then we derive the optimalthreshold by using point-to-point Bayesian threshold.We use wavelet threshold to denoise the original noising image so that we get the stepless image which retains image detail information. Finally, the edge image is embedded into the smoothing image, and the final denoising image is gained. Theoretical analysis and experimental results show that the algorithms can remove noise effectively while retaining the edge information better and increasing PSNR.