基于改进能量泛函模型的噪声图像分割算法  

Noise image segmentation algorithm based on improved energy functional model

作　　者：韩明 吴朔媚 王敬涛 孟军英 HAN Ming;WU Shuomei;WANG Jingtao;MENG Junying(School of Computer Science and Engineering,Shijiazhuang University,Shijiazhuang 050035,China)

机构地区：[1]石家庄学院计算机科学与工程学院,石家庄050035

出　　处：《计算机工程与应用》2018年第23期23-30,共8页Computer Engineering and Applications

基　　金：河北省高等学校科学技术研究资助项目(No.BJ2017105;No.Z2017051);河北省科技计划支撑项目(No.16222101D);石家庄市重点研发项目(No.181230041A)

摘　　要：针对噪声图像的分割难,分割不准确,以及现有模型无法适应多种噪声环境的问题,提出了一种基于改进的能量泛函模型的噪声图像分割算法,该算法结合各向异性扩散方程和灰度水平集算法,通过对能量泛函的改进实现对噪声图像的准确、快速分割。将非凸泛函引入能量泛函模型,并通过证明不存在全局最小值,利用获得的能量泛函模型得到光滑的目标图像边界。将各向异性扩散模型得到的光滑图像与水平集模型相结合,得到改进的能量泛函模型。通过求解在能量泛函的最小值,得到前景目标的水平集演化的最佳位置。该算法与同类模型的对比实验表明该模型对噪声图像具有较高的分割精度和鲁棒性。As to the problems of difficult and inaccurate in noise image segmentation,and the existing models are poor in robustness to complex noise environment.This paper proposes the noise image segmentation algorithm based on improved energy functional model,which combines anisotropic diffusion equation and level set method.Through the improvement of energy functional it can segment noise images accurately and quickly.Firstly,non-convex functional is introduced into the energy functional model,and it is proved that there is no global minimum in the energy functional model.Object outline is detected by energy functional model based on non-convex functional.Secondly,smooth images are obtained by anisotropic diffusion model,the improved energy function model is obtained by combining the smooth images and level set model.Finally,The optimal position of the level set evolution of the foreground target is obtained by solving the minimum value in the energy functional.The experimental results show that the proposed algorithm has higher segmentation accuracy and robust for noise image than other similar models.

关 键 词：图像分割 噪声 能量泛函 水平集 各向异性扩散方程 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]