Blind Image Deconvolution using Deep Generative Priors

This paper proposes a new framework to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution problem by using deep generative priors. 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. The regularized problem is efficiently solved by simple alternating gradient descent algorithm operating in the latent lower-dimensional space of each generative model. We empirically show that by doing so, excellent image deblurring results are achieved even under extravagantly large blurs, and heavy noise. Our proposed method is in stark contrast to the conventional end-to-end approaches, where a deep neural network is trained on blurred input, and the corresponding sharp output images while completely ignoring the knowledge of the underlying forward map (convolution operator) in image blurring.