融合局部和非局部信息的自适应贝叶斯分割方法
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摘要
传统基于马尔可夫随机场(MRF)的贝叶斯分割方法由于只考虑邻域像素点的先验影响,无法有效抑制相干斑噪声;边缘区域分割效果欠佳,因为先验模型假定邻域中每个像素对中心像素的影响相同。因而,该文提出一种融合局部和非局部信息的自适应贝叶斯分割方法。针对SAR图像中的相干斑噪声模型,引入基于比率概率的相似性测度,用非局部相似像素块指导当前像素点的分割;并且采用变分系数(Coefficient of Variation,CV)方法获取边缘区域图像模板,在边缘区域自适应地调整定义的结构指数以及搜索窗尺寸,从而改善分割过度平滑与结构保持的矛盾;在实验分析中,利用新方法对部分图像进行了分割实验,并与传统方法作了比较。改进方法的分割结果形状更为准确,不但抑制了相干斑噪声,还有效保持了细节特征,具有显著优势。
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.
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.
引文
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[13]李光廷,杨亮,黄平平,等.SAR图像相干斑抑制中的像素相关性测量[J].雷达学报,2012,1(3):301-308.Li Guang-ting,Yang Liang,Huang Ping-ping,et al..The pixel-similarity measurement in SAR image despeckling[J].Journal of Radars,2012,1(3):301-308.
[14]Feng Jie,Jiao Li-cheng,Zhang Xiang-rong,et al..Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation[J].Signal Processing,2013,93(2):487-499.
[15]Otsu N.A threshold selection method from gray-level histogram[J].IEEE Transactions on System,Man,and Cybernetic,1979,9(1):62-66.
[16]Li Qing-sheng and Liu Guo-ying.Multi-resolution Markov random field model with variable potentials in wavelet domain for texture image segmentation[C].International Conference on Computer Application and System Modeling,Taiyuan,2010:342-346.
[17]Gan L,Wu Y,Liu M,et al..Triplet Markov fields with edge location for fast unsupervised multi-class segmentation of synthetic aperture radar images[J].IET Image Processing,2012,6(7):831-838.
[2]Zhang Peng,Li Ming,Wu Yan,et al..SAR image multiclass segmentation using a multiscale TMF model in wavelet domain[J].IEEE Geoscience and Remote Sensing Letters,2012,9(6):1099-1103.
[3]刘国英,马国锐,王雷光.基于Markov随机场的小波域图像建模及分割Matlab环境[M].北京:科学出版社,2010:8-14.
[4]Dong Y and Forster B C.Segmentation of radar imagery using Gaussian Markov random field models and wavelet and transform technique[C].International Geoscience and Remote Sensing Symposium,Singapore,1997:2054-2056.
[5]侯一民,郭雷.一种基于马尔可夫随机场的SAR图像分割新方法[J].电子与信息学报,2007,29(5):1069-1072.Hou Yi-min and Guo Lei.A novel SAR image segmentation method based on Markov random field[J].Journal of Electronics&Information Technology,2007,29(5):1069-1072.
[6]Zheng Chen,Wang Lei-guang,Chen Rong-yuan,et al..Image segmentation using multiregion-resolution MRF model[J].IEEE Geoscience and Remote Sensing Letters,2013,10(4):816-820.
[7]He Fei-yue,Tian Zheng,Liu Xiang-zeng,et al..A fast edge tracking algorithm for image segmentation using a simple Markov random field model[C].International Conference on Computer Science and Electronics Engineering,Hangzhou,2012:633-636.
[8]Buades A,Coll B,and Morel J M.A non-local algorithm for image denoising[C].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,2005:60-65.
[9]Geman S and Geman D.Stochastic relaxation,Gibbs distributions,and the Bayesian restoration of images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):721-741.
[10]Deledalle C,Denis L,and Tupin F.Iterative weighted maximum likelihood denoising with probabilistic patch-based weights[J].IEEE Transactions on Image Processing,2009,18(12):2661-2672.
[11]CoupéP,Hellier P,Kervrann C,et al..Bayesian non-local means-based speckle filtering[C].5th IEEE International Symposium on Biomedical Imaging:From Nano to Macro,Paris,2008:1291-1294.
[12]Feng Hong-xiao,Hou Biao,and Gong Mao-guo.SAR image despeckling based on local homogeneous-region segmentation by using pixel-relativity measurement[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(7):2724-2737.
[13]李光廷,杨亮,黄平平,等.SAR图像相干斑抑制中的像素相关性测量[J].雷达学报,2012,1(3):301-308.Li Guang-ting,Yang Liang,Huang Ping-ping,et al..The pixel-similarity measurement in SAR image despeckling[J].Journal of Radars,2012,1(3):301-308.
[14]Feng Jie,Jiao Li-cheng,Zhang Xiang-rong,et al..Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation[J].Signal Processing,2013,93(2):487-499.
[15]Otsu N.A threshold selection method from gray-level histogram[J].IEEE Transactions on System,Man,and Cybernetic,1979,9(1):62-66.
[16]Li Qing-sheng and Liu Guo-ying.Multi-resolution Markov random field model with variable potentials in wavelet domain for texture image segmentation[C].International Conference on Computer Application and System Modeling,Taiyuan,2010:342-346.
[17]Gan L,Wu Y,Liu M,et al..Triplet Markov fields with edge location for fast unsupervised multi-class segmentation of synthetic aperture radar images[J].IET Image Processing,2012,6(7):831-838.