  We propose a state-of-the-art method for super-resolution with non-uniform
blur. Single-image super-resolution methods seek to restore a high-resolution
image from blurred, subsampled, and noisy measurements. Despite their
impressive performance, existing techniques usually assume a uniform blur
kernel. Hence, these techniques do not generalize well to the more general case
of non-uniform blur. Instead, in this paper, we address the more realistic and
computationally challenging case of spatially-varying blur. To this end, we
first propose a fast deep plug-and-play algorithm, based on linearized ADMM
splitting techniques, which can solve the super-resolution problem with
spatially-varying blur. Second, we unfold our iterative algorithm into a single
network and train it end-to-end. In this way, we overcome the intricacy of
manually tuning the parameters involved in the optimization scheme. Our
algorithm presents remarkable performance and generalizes well after a single
training to a large family of spatially-varying blur kernels, noise levels and
scale factors.

Computer Vision and Pattern Recognition;Image and Video Processing