  Deep image inpainting research mainly focuses on constructing various neural
network architectures or imposing novel optimization objectives. However, on
the one hand, building a state-of-the-art deep inpainting model is an extremely
complex task, and on the other hand, the resulting performance gains are
sometimes very limited. We believe that besides the frameworks of inpainting
models, lightweight traditional image processing techniques, which are often
overlooked, can actually be helpful to these deep models. In this paper, we
enhance the deep image inpainting models with the help of classical image
complexity metrics. A knowledge-assisted index composed of missingness
complexity and forward loss is presented to guide the batch selection in the
training procedure. This index helps find samples that are more conducive to
optimization in each iteration and ultimately boost the overall inpainting
performance. The proposed approach is simple and can be plugged into many deep
inpainting models by changing only a few lines of code. We experimentally
demonstrate the improvements for several recently developed image inpainting
models on various datasets.
