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单幅图像深度估计是目前计算机视觉和计算机图形学中的一个挑战性问题.基于柯西分布的点扩散函数模型(point spread function,PSF),给出了一种以计算物体图像边沿散焦模糊量实现单幅图像场景深度估计的方法.将原始散焦图像分别用两个柯西分布核重新模糊,用两模糊图像的梯度比值计算边沿散焦模糊量,得到稀疏深度估计,再通过内插方法取得场景的全深度估计.多种真实场景图像的实验结果表明,本方法能够从非标定单幅散焦图像中较好地估计场景深度,且对图像噪声、边沿误差和邻近边沿的干扰具有较好的鲁棒性,综合性能优于现有基于高斯PSF模型的方法;同时,验证了也可用非高斯模型建模PSF.“,”Scene depth estimation from a single image is a challenging problem in the field of computer vision and computer graphics.We present an approach to estimate the amount of defocus blur at edges based on the Cauchy distribution model for the DSF (points spread function).The input image was reblurred twice respectively using two Cauchy distribution kernels,and the amount of defocus blur at the edges was obtained by the two scale parameters and the ratio between the gradients of the two reblurred images.By propagating the blur amount at edges to the entire image,a full depth map was obtained.Experimental results on several real images demonstrate the effectiveness of our method in providing reliable depth estimation.Our method is robust to image noise,inaccurate edge location,and interference from neighboring edges,and its performance is better than existing methods based on a Gaussian DSF model.The results verify that a non Gaussian model for DSF is feasible.