基于高斯-柯西混合模型的单幅散焦图像深度恢复方法

Depth Estimation from Single Defocused Image Based on Gaussian-Cauchy Mixed Model

单幅图像场景深度的获取一直是计算机视觉领域的一个难题。使用高斯分布函数或柯西分布函数近似点扩散函数模型(PSF),再根据图像边缘处散焦模糊量的大小与场景深度之间的关系估算出深度信息,是一种常用的方法。真实世界中图像模糊的缘由千变万化,高斯分布函数以及柯西分布函数并不一定是最佳的近似模型,并且传统的方法对于图像存在阴影、边缘不明显以及深度变化比较细微的区域的深度恢复结果不够准确。为了提取更为精确的深度信息,提出一种利用高斯-柯西混合模型近似PSF的方法;然后对散焦图像进行再模糊处理,得到两幅散焦程度不同的图像;再通过计算两幅散焦图像边缘处梯度的比值估算出图像边缘处的散焦模糊量,从而得到稀疏深度图;最后使用深度扩展法得到场景的全景深度图。通过大量真实图像的测试,说明新方法能够从单幅散焦图像中恢复出完整、可靠的深度信息,并且其结果优于目前常用的两种方法。

Recovering the 3D depth of a scene from a single image is a difficult problem in the field of computer vision. Most methods for depth estimation from a single defocused image construct the point spread function by an 2D Gaussian or Cauchy distribution. However, reasons of blurred images in the real world are varied, so a simple Gaussian or Cauchy distribution function may be not the best approximation model. They are often influenced by noise and inaccurate edge location, and then a high quality depth estimation may be difficult to achieve. A Gaussian-Cauchy mixed distribution model was presented in this paper to re-blur the given defocused image, and two different degree blurred images were then obtained. We estimated the sparse depth map generated from the gradients ratio at edge locations by the two blurred images. In so doing,a full depth map can be recovered by matting Laplacian interpolation. Experimental results on some real images demonstrate that the proposed approach is effective and better than the two commonly used ap- proaches.