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Deep image inpainting research mainly focuses on constructing various neuralnetwork architectures or imposing novel optimization objectives. However, onthe one hand, building a state-of-the-art deep inpainting model is an extremelycomplex task, and on the other hand, the resulting performance gains aresometimes very limited. We believe that besides the frameworks of inpaintingmodels, lightweight traditional image processing techniques, which are oftenoverlooked, can actually be helpful to these deep models. In this paper, weenhance the deep image inpainting models with the help of classical imagecomplexity metrics. A knowledge-assisted index composed of missingnesscomplexity and forward loss is presented to guide the batch selection in thetraining procedure. This index helps find samples that are more conducive tooptimization in each iteration and ultimately boost the overall inpaintingperformance. The proposed approach is simple and can be plugged into many deepinpainting models by changing only a few lines of code. We experimentallydemonstrate the improvements for several recently developed image inpaintingmodels on various datasets.

Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions