  Defocus blur is one kind of blur effects often seen in images, which is
challenging to remove due to its spatially variant amount. This paper presents
an end-to-end deep learning approach for removing defocus blur from a single
image, so as to have an all-in-focus image for consequent vision tasks. First,
a pixel-wise Gaussian kernel mixture (GKM) model is proposed for representing
spatially variant defocus blur kernels in an efficient linear parametric form,
with higher accuracy than existing models. Then, a deep neural network called
GKMNet is developed by unrolling a fixed-point iteration of the GKM-based
deblurring. The GKMNet is built on a lightweight scale-recurrent architecture,
with a scale-recurrent attention module for estimating the mixing coefficients
in GKM for defocus deblurring. Extensive experiments show that the GKMNet not
only noticeably outperforms existing defocus deblurring methods, but also has
its advantages in terms of model complexity and computational efficiency.
