散焦含噪图像的点扩散函数估计与边缘检测  

PSF Estimation and Edge Detection of Defocused Noisy Image

作　　者：吴俊芳[1] 刘桂雄[1] 韦宁[1] 

机构地区：[1]华南理工大学机械与汽车工程学院,广东广州510640

出　　处：《华南理工大学学报（自然科学版）》2010年第6期118-121,133,共5页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(50805053)

摘　　要：针对机器视觉中图像模糊的原因,采用广义高斯函数描述点扩散函数(PSF),建立边缘扩散模型,从一幅图像所含的多条边缘中提取样本,依据提出的整合准则从全部样本的估计结果中计算整幅图像的PSF,利用最优求解的概念,在最小二乘近似下实现PSF及亚像素级边缘位置的最佳估计.实验结果表明,由于获得了图像对应的PSF最佳估计,因此边缘检测具有比较高的抗噪性能,在边缘亚像素检测基础上完成的尺寸测量误差小于0.5%.By considering the causes of image blur in machine vision,the point spread function（PSF） is described with the generalized Gaussian function,and an blurred edge model is established.Then,some samples are obtained from the edges of an image.Moreover,an integration rule is proposed to calculate the PSF of the image according to the estimated results of all the samples.Finally,the optimal PSF and sub-pixel level edge position are estimated via the least squares approximation.Experimental results show that the proposed edge detection algorithm is robust to noise due to the accurate estimation of PSF,and that the dimension error based on the sub-pixel-level edge detection is less than 0.5%.

关 键 词：图像处理 边缘检测 点扩散函数 最小二乘近似 亚像素 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]