Depth estimation from a single defocused image using multi-scale kernels

Abstract

Depth estimation from defocus (DFD) has proved to be an efficient way to recover depth information based on the blur amount of defocus images. By introducing a multi-scale strategy into DFD, a novel depth estimation method from a single defocused image is proposed in this paper. The original input image is re-blurred using Gaussian kernels with different scale parameters, then a robust estimation of defocus blur amount at edge locations could be obtained by calculating the gradient magnitude ratio according to the original and re-blurred images. Dense defocus maps are generated via global interpolation and refinement and hence depth can be obtained under certain camera parameters. Experimental results demonstrate the effectiveness of the proposed method on obtaining high quality dense defocus and depth maps.