The Application Research Of Wavelet Transform In Wood Cell Image Edge Detection

Along with the developing of wood science, it has become an important direction of wood research to analysis wood cell by Computer Image Processing. The edge information of cell image is very useful, which is the basis of cell image segment and an accurate approach of all important parameters to reflect wood's property.In this paper, we introduce the application of some typical image edge detection algorithm, and then determine the methods adapting to wood cell image edge detection. Mostly, traditional arithmetic of image edge detection distill edge using gradient maximum or zero-crossing which base on gray discontinuousness on edge. They are affected easily by noise. Canny arithmetic has shortcomings such as big computing and confirming positions inaccur-ately. Image edge detection based on multi-resolution wavelet transform makes up these shortages before.According to the criterion of edge detection and consulting the design-aim of optimal edge-filter, we come out with the best edge detection wavelet, namely Quadratic B-Spline wavelet. We not only illustrate the superiority of quadratic spline wavelet edge detection's arithmetic, but also prove quadratic spline wavelet are optimum edge detection's arithmetic based on Canny optimum criterions of edge detection.Then, according to the application of wavelet transform in wood cell image edge detection, we define quadratic spline as wavelet and put forward the principle of quadratic B-Spline wavelet multi-scale quick edge detection algorithm. Application of the algorithm of the microscopic image of wood cells multi-scale edge detection, the results show that they are improver than traditional edge detection algorithm and get good edge detection effect, but also exist imperfections.In the last, an edge detection algorithm based on image fusion is proposed. Firstly, the source image is decomposed by wavelet, using wavelet modulus maxima algorithm extracts edges in high-frequency images on the different levels and using mathematical morphological method extracts edges in low-frequency image, then the two edge images are fused according to some fusion rules. The experimental results show that the approach is superior to wavelet modulus maxima algorithm or mathematical morphological method alone and is good robust to noise and the edge is consecutive and clear.