An Adaptive Bayesian Segmentation Method Fused of Local and Non-local Information

With only considering the impact of neighborhood pixels, the traditional Bayesian segmentation method based on Markov Random Field (MRF) can not suppress the speckle noise effectively. In the traditional priori model, the influence of each pixel within the neighborhood to the center one is assumed the same, which makes the description of the edge imprecise and the segmentation ineffective. Thus, an adaptive Bayesian segmentation method fused of local and non-local information is proposed. For the multiplicative noise model contained in SAR image, the similarity measure based on ratio probability is introduced, and the nonlocal similar pixel-blocks are adopted to guide the segmentation of the current pixel. Furthermore, the Coefficient of Variation (CV) method is employed to obtain the image template of edge area. In the edge region, the structure index and the size of search window are adaptively adjusted to improve the inconsistency between excessive smooth and structure preserving. In the experimental analysis, parts of the SAR image segmentation results with the new technique are given, which are compared with the traditional means. There is a significant advantage that the proposed algorithm enables more accurate segmentation results, which not only make the speckle noise suppressed, but also keep the detail characteristics effectively.