Essentially most commonly-used denoising methods use low pass filter to get rid of the noise. But both edge and noise information are high frequency information

so the loss of edge information is evident and inevitable in the denoising process. Edge information is the most important high frequency information of an image. Therefore we should try to maintain more edge information in the process of denoising. Thus comes out the idea of this paper. We present a new image denoising method:wavelet image threshold denoising based on edge detection. Before denoising

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 and therefore we can set the denoising thresholds only based on the noise variances without damaging the image's edges. The theoretical analysis and experimental results presented in this paper show that

compared with the commonly-used wavelet threshold denoising methods

our denoising method can keep image's edges from damaging and increase PSNR up to 1~2dB. Finally we can draw the conclusion:Edge detection and denoising are two important branches of image processing. If we combine edge detection with denoising

we can overcome the shortcoming of the commonly-used denoising methods and do denoising without blurring the edge notably.