  The defocus deblurring raised from the finite aperture size and exposure time
is an essential problem in the computational photography. It is very
challenging because the blur kernel is spatially varying and difficult to
estimate by traditional methods. Due to its great breakthrough in low-level
tasks, convolutional neural networks (CNNs) have been introduced to the defocus
deblurring problem and achieved significant progress. However, they apply the
same kernel for different regions of the defocus blurred images, thus it is
difficult to handle these nonuniform blurred images. To this end, this study
designs a novel blur-aware multi-branch network (BaMBNet), in which different
regions (with different blur amounts) should be treated differentially. In
particular, we estimate the blur amounts of different regions by the internal
geometric constraint of the DP data, which measures the defocus disparity
between the left and right views. Based on the assumption that different image
regions with different blur amounts have different deblurring difficulties, we
leverage different networks with different capacities (\emph{i.e.} parameters)
to process different image regions. Moreover, we introduce a meta-learning
defocus mask generation algorithm to assign each pixel to a proper branch. In
this way, we can expect to well maintain the information of the clear regions
while recovering the missing details of the blurred regions. Both quantitative
and qualitative experiments demonstrate that our BaMBNet outperforms the
state-of-the-art methods. Source code will be available at
https://github.com/junjun-jiang/BaMBNet.
