Image Denoising Based on adaptive Morphological Edge Detection and Wavelet Fusion

The effect of traditional wavelet denoising algorithms is not very good and the detail precision of the image isn't high enough. What is worse, it will damage the edge and corner information of the image, and lose texture details. To solve problems above, a new method based on adaptive morphological edge detection and wavelet fusion is proposed. Firstly, the noisy image is decomposed with two wavelet bases. Then we divide the wavelet coefficients into two parts by using the adaptive morphological edge detection method. Secondly, we deal the wavelet coefficients of the edge by using the improved threshold and the hard threshold function. Thirdly, we deal the others by using the improved wavelet threshold and the improved threshold function. At last, we obtain the denoising image by using the wavelet fusion algorithm. Results of the experiment show that the new method can not only highlight the characteristics of the image texture, but also can remove the noise without hurting the important characteristics and the texture edges at the same time. So the new method has great application valu