  Video deblurring for hand-held cameras is a challenging task, since the
underlying blur is caused by both camera shake and object motion.
State-of-the-art deep networks exploit temporal information from neighboring
frames, either by means of spatio-temporal transformers or by recurrent
architectures. In contrast to these involved models, we found that a simple
baseline CNN can perform astonishingly well when particular care is taken
w.r.t. the details of model and training procedure. To that end, we conduct a
comprehensive study regarding these crucial details, uncovering extreme
differences in quantitative and qualitative performance. Exploiting these
details allows us to boost the architecture and training procedure of a simple
baseline CNN by a staggering 3.15dB, such that it becomes highly competitive
w.r.t. cutting-edge networks. This raises the question whether the reported
accuracy difference between models is always due to technical contributions or
also subject to such orthogonal, but crucial details.
