  This paper proposes a novel approach to regularize the \textit{ill-posed} and
\textit{non-linear} blind image deconvolution (blind deblurring) using deep
generative networks as priors. We employ two separate 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 promising deblurring
results on images even under large blurs, and heavy noise. To address the
shortcomings of generative models such as mode collapse, we augment our
generative priors with classical image priors and report improved performance
on complex image datasets. The deblurring performance depends on how well the
range of the generator spans the image class. Interestingly, our experiments
show that even an untrained structured (convolutional) generative networks acts
as an image prior in the image deblurring context allowing us to extend our
results to more diverse natural image datasets.
