Cauchy Distribution Based Depth Map Estimation from a Single Image

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 re-blurred 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 re-blurred 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.