Abstract
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-networkbased 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-thearts, both quantitatively and qualitatively