  Blind image deblurring remains a challenging problem for modern artificial
neural networks. Unlike other image restoration problems, deblurring networks
fail behind the performance of existing deblurring algorithms in case of
uniform and 3D blur models. This follows from the diverse and profound effect
that the unknown blur-kernel has on the deblurring operator.
  We propose a new architecture which breaks the deblurring network into an
analysis network which estimates the blur, and a synthesis network that uses
this kernel to deblur the image. Unlike existing deblurring networks, this
design allows us to explicitly incorporate the blur-kernel in the network's
training. In addition, we introduce new cross-correlation layers that allow
better blur estimations, as well as unique components that allow the estimate
blur to control the action of the synthesis deblurring action.
  Evaluating the new approach over established benchmark datasets shows its
ability to achieve state-of-the-art deblurring accuracy on various tests, as
well as offer a major speedup in runtime.
