摘要
为了提高强噪声污染图像分割的鲁棒性,给出一种改进的非局部模糊聚类图像分割算法。改进算法将模糊因子的局部邻域值替换为非局部均值滤波图像的像素值,并加入局部空间信息,产生新的目标函数。借助拉格朗日乘子法,从最小化目标函数得出隶属度和聚类中心的迭代公式,进而完成图像分割。对合成图像、医学图像和自然图像添加高斯噪声、莱斯噪声和椒盐噪声,用于分割测试,结果显示,改进算法对强噪声图像具有更高的正确分割率和较小的模糊性。
The non-local fuzzy clustering image segmentation algorithm is revised to improve the robustness of image segmentation with strong noise pollution.The local neighborhood value of fuzzy factor is replaced by the pixel value of non-local mean filter image,and the local spatial information is introduced in fuzzy factor to generate a new objective function.With the help of Lagrange multiplier method,the iterative formulae of membership degree and clustering center are obtained from the optimal fuzzy clustering objective function,with which the image segmentation is completed.segmentation test on some synthetic images,medical images and natural images which are added Gauss noise,Rician noise and salt and pepper noise separately are experimented,and the results show that the revised algorithm is of higher correct segmentation rate and smaller ambiguity.
引文
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