结合局部灰度差异的噪声图像分割模型  

Noise image segmentation model with local intensity difference

作　　者：李钢[1] 李海芳[1] 尚方信 郭浩[1] 

机构地区：[1]太原理工大学计算机科学与技术学院,太原030024

出　　处：《计算机应用》2018年第3期842-847,890,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61402318)~~

摘　　要：针对未知强度和分布规律的噪声图像难以得到正确分割,现有模型无法适应多种噪声环境的问题,提出了一种基于图像局部灰度差异的噪声图像分割模型。首先,分析局部K均值聚类(LCK)模型和局部相似性系数(RLSF)模型中能量泛函对噪声点的降权机制的不足,提出优化方案;其次,将一种结合局部灰度差异的噪声点修复函数引入能量泛函,降低了显著偏离局部均值的噪声点对分割结果的干扰;最后使用变分法推导出该模型的水平集迭代方程。与局部二值拟合(LBF)模型、LCK模型和RLSF模型相比,使用该模型进行噪声自然图像分割时,可得到更高的查全率、查准率和F值。实验结果表明,所提模型可稳定、有效地分割非均匀和高噪声图像。It is difficult to get correct segmentation results of the images with unknown intensity and distribution of noise, and the existing models are poor in robustness to complex noise environment. Thus, a noise adaptive algorithm for image segmentation was proposed based on local intensity difference. Firstly, Local Correntropy-based K-means （LCK） model and Region-based model via Local Similarity Factor （RLSF） model were analyzed to reduce the sensitivity to noise pixels. Secondly, a correction function based on local intensity statistical information was introduced to reduce the interference of samples to be away from local mean to segmentation results. Finally, the active contour energy function and iterative equation integrated with the correction function were deduced. Experimental results performed on synthetic, and real-world noisy images show that the proposed model is more robust with higher precision, recall and F-score in comparison with Local Binary Fitting （LBF） model, LCK model and RLSF model, and it can achieve good performance on the images with intensity inhomogeneity and noise.

关 键 词：图像分割 图像噪声 活动轮廓模型 局部灰度差异 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]