计算机科学 ›› 2017, Vol. 44 ›› Issue (1): 32-36.doi: 10.11896/j.issn.1002-137X.2017.01.006
薛松,王文剑
XUE Song and WANG Wen-jian
摘要： 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。
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