  This paper proposes a novel approach to regularize the ill-posed blind image
deconvolution (blind image deblurring) problem using deep generative networks.
We employ two separate deep generative models - one trained to produce sharp
images while the other trained to generate blur kernels from lower dimensional
parameters. To deblur, we propose an alternating gradient descent scheme
operating in the latent lower-dimensional space of each of the pretrained
generative models. Our experiments show excellent deblurring results even under
large blurs and heavy noise. To improve the performance on rich image datasets
not well learned by the generative networks, we present a modification of the
proposed scheme that governs the deblurring process under both generative and
classical priors.
