  Reproducing an all-in-focus image from an image with defocus regions is of
practical value in many applications, eg, digital photography, and robotics.
Using the output of some existing defocus map estimator, existing approaches
first segment a de-focused image into multiple regions blurred by Gaussian
kernels with different variance each, and then de-blur each region using the
corresponding Gaussian kernel. In this paper, we proposed a blind deconvolution
method specifically designed for removing defocus blurring from an image, by
providing effective solutions to two critical problems: 1) suppressing the
artifacts caused by segmentation error by introducing an additional variable
regularized by weighted $\ell_0$-norm; and 2) more accurate defocus kernel
estimation using non-parametric symmetry and low-rank based constraints on the
kernel. The experiments on real datasets showed the advantages of the proposed
method over existing ones, thanks to the effective treatments of the two
important issues mentioned above during deconvolution.

Computer Vision and Pattern Recognition