  Blind deconvolution is a classical yet challenging low-level vision problem
with many real-world applications. Traditional maximum a posterior (MAP) based
methods rely heavily on fixed and handcrafted priors that certainly are
insufficient in characterizing clean images and blur kernels, and usually adopt
specially designed alternating minimization to avoid trivial solution. In
contrast, existing deep motion deblurring networks learn from massive training
images the mapping to clean image or blur kernel, but are limited in handling
various complex and large size blur kernels. To connect MAP and deep models, we
in this paper present two generative networks for respectively modeling the
deep priors of clean image and blur kernel, and propose an unconstrained neural
optimization solution to blind deconvolution. In particular, we adopt an
asymmetric Autoencoder with skip connections for generating latent clean image,
and a fully-connected network (FCN) for generating blur kernel. Moreover, the
SoftMax nonlinearity is applied to the output layer of FCN to meet the
non-negative and equality constraints. The process of neural optimization can
be explained as a kind of "zero-shot" self-supervised learning of the
generative networks, and thus our proposed method is dubbed SelfDeblur.
Experimental results show that our SelfDeblur can achieve notable quantitative
gains as well as more visually plausible deblurring results in comparison to
state-of-the-art blind deconvolution methods on benchmark datasets and
real-world blurry images. The source code is available at
https://github.com/csdwren/SelfDeblur
