Wavelet Image Threshold Denoising Based on Edge Detection

Most commonly used denoising methods use low pass filters to get rid of noise. However, both edge and noise information is 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, so we should try to maintain more edge information while denoising. From this comes the thesis of this paper. In it, we present a new image denoising method: wavelet image threshold denoising based on edge detection. Before denoising, those wavelet coefficients of an image that correspond to an image's edges are first detected by wavelet edge detection. The detected wavelet coefficients will then be protected from denoising, and we can therefore set the denoising thresholds based solely on the noise variances, without damaging the image's edges. The theoretical analyses and experimental results presented in this paper show that, compared to commonly-used wavelet threshold denoising methods, our method can keep an image's edges from damage and can increase the PSNR up to 12dB. Finally, we can draw the conclusion that edge detection and denoising are two important branches of image processing. If we combine edge detection with denoising, we can overcome the shortcomings of commonly-used denoising methods and do denoising without notably blurring the edge.