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%.