Blur Calibration for Depth from Defocus

Depth from defocus based methods rely on measuring the depth dependent blur at each pixel of the image. A core component in the defocus blur estimation process is the depth variant blur kernel. This blur kernel is often approximated as a Gaussian or pillbox kernel which only works well for small amount of blur. In general the blur kernel depends on the shape of the aperture and can vary a lot with depth. For more accurate blur estimation it is necessary to precisely model the blur kernel. In this paper we present a simple and accurate approach for performing blur kernel calibration for depth from defocus. We also show how to estimate the relative blur kernel from a pair of defocused blur kernels. Our proposed approach can estimate blurs ranging from small (single pixel) to sufficiently large (e.g. 77 x 77 in our experiments). We also experimentally demonstrate that our relative blur estimation method can recover blur kernels for complex asymmetric coded apertures which has not been shown before.

Paper

Poster

Relative Blur Examples

Code