小波多尺度改进Chan-Vese模型的红外图像分割  

Improved Chan-Vese Model Based on Wavelet Multiscale Transform for Infrared Image Segmentation

作　　者：倪超[1,2] 李奇[2] 夏良正[2] 

机构地区：[1]南京林业大学机电工程学院,南京210037 [2]东南大学自动化学院,南京210096

出　　处：《光电工程》2009年第7期94-99,共6页Opto-Electronic Engineering

基　　金：南京林业大学高学历人才基金项目(B2008-27)

摘　　要：针对红外图像特点,提出了一种小波多尺度改进Chan-Vese模型的红外图像分割算法。首先,利用小波多尺度分析的良好信噪分离性能提取红外图像的有效边缘信息,将边缘信息添加到Chan-Vese模型的能量函数中从而提高模型的局部控制能力。同时增加内部变形能量项,约束水平集函数逼近符号距离函数,避免了水平集函数的重新初始化过程,改进了Chan-Vese模型。然后,从小波变换的顶层低频子带图像开始逐层采用改进的Chan-Vese模型分割图像,并将分割结果通过插值方式传递至下一层作为分割的初始轮廓,最终实现红外图像的分割,具有抗噪性能强和运算速度快的特点。According to infrared image characteristics, an infrared image segmentation method for improved Chan-Vese model based on wavelet multisale transform is presented. First, edge information is extracted by using wavelet multiscale transform, and it is added into energy function of Chan-Vese model to improve local control ability. Furthermore, internal energy term is added, which constraints level set function to approach signed distance function so as to avoid reinitialization of level set function. Then, wavelet low frequency subbands are segmented with improved Chan-Vese model from top to bottom, and segmented results are interpolated into next subbands as initiative contours. Finally, the method can realize infrared image segmentation. Experiments show that the method is very efficient and robust to noise.

关 键 词：小波多尺度 CHAN-VESE模型 红外图像 边缘信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]