  In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually
restoring the sharp image on different resolutions in a pyramid, is very
successful in both traditional optimization-based methods and recent
neural-network-based approaches. In this paper, we investigate this strategy
and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task.
Compared with the many recent learning-based approaches in [25], it has a
simpler network structure, a smaller number of parameters and is easier to
train. We evaluate our method on large-scale deblurring datasets with complex
motion. Results show that our method can produce better quality results than
state-of-the-arts, both quantitatively and qualitatively.
