基于边缘检测的图象小波阈值去噪方法

边缘特征是图象最为有用的高频信息，因此，在图象去噪的同时，尽量保留图象的边缘特征，应是图象去噪首要顾及的问题。基于这一思想，提出了基于边缘检测的图象小波阈值去噪方法。该方法在去噪之前，先通过小波边缘检测方法确定哪些小波系数是图象的边缘特征，这些小波系数将不受阈值去噪的影响，因此，可以只是根据噪声方差来设置去噪的阈值，而不必担心损害图象的边缘特征。理论分析和实验结果都表明，与普通的小波阈值去噪方法相比，该方法不但可以保持图象的边缘信息，而且能提高去噪后图象的峰值信噪比1-2dB。要做到既去除图象噪声，又不模糊图象边缘特征是很困难的。该方法把去噪和边缘检测结合起来，在一定程度上解决了这种两难的问题。

Wavelet Image Threshold Denoising Based on Edge Detection

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.