结合边缘信息的多尺度MRF图像分割

针对采用多尺度马尔可夫随机场分割图像后还存在误分类的特点，提出了结合边缘信息进一步减少误分类，并设计了一种基于平稳小波变换、两个相邻尺度相乘的边缘提取算法。分析和实验结果表明，该边缘提取算法达到了既能提取出较完整、真实的边缘，又较好地抑制噪声，计算量少的要求。在分割算法中结合提取出的边缘信息，使图像在均匀区域中的误分类大大减少，得到了更好的分割结果，而增加的计算量只是由边缘提取带来的．该边缘提取算法和结合边缘的图像分割算法更适合于强的噪声图像。

Multiscale MRF Based Image Segmentation Associate with Edge Information

Since segmented noisy image using multiscale markov random fields(MRF) still has very small error blocks or single error pixel in the smooth area,this paper proposes an approach associate edge information with multiscale MRF to reduce error classification.Because images are corrupted by the white gaussian noise,edge is difficult to detect successfully.To detect edge,an algorithm based on the stationary wavelet transform and multiplication between neighbour scales is designed. When there is a significant feature at some position,the wavelet coefficients at that position across scales have high correlation.Such correlation can be taken by multiplication between scales,so edge can be located better.Analysis and experiments demonstrate that the proposed edge detection algorithm is effective and fast.Better results are obtained using the segmentation algorithm associate with edge,most error classification among the smooth image area is corrected.The amount of added computation is only brought by the expense of the edge detection procedure.These new edge detection and segmentation algorithm are more fit for strong noisy images.