  Blurry images usually exhibit similar blur at various locations across the
image domain, a property barely captured in nowadays blind deblurring neural
networks. We show that when extracting patches of similar underlying blur is
possible, jointly processing the stack of patches yields superior accuracy than
handling them separately. Our collaborative scheme is implemented in a neural
architecture with a pooling layer on the stack dimension. We present three
practical patch extraction strategies for image sharpening, camera shake
removal and optical aberration correction, and validate the proposed approach
on both synthetic and real-world benchmarks. For each blur instance, the
proposed collaborative strategy yields significant quantitative and qualitative
improvements.
