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Recent deep learning based image inpainting methods which utilize contextualinformation and two-stage architecture have exhibited remarkable performance.However, the two-stage architecture is time-consuming, the contextualinformation lack high-level semantics and ignores both the semantic relevanceand distance information of hole's feature patches, these limitations result inblurry textures and distorted structures of final result. Motivated by theseobservations, we propose a new deep generative model-based approach, whichtrains a shared network twice with different targets and utilizes a singlenetwork during the testing phase, so that we can effectively save inferencetime. Specifically, the targets of two training steps are structurereconstruction and texture generation respectively. During the second training,we first propose a Pyramid Filling Block (PF-block) to utilize the high-levelfeatures that the hole regions has been filled to guide the filling process oflow-level features progressively, the missing content can be filled from deepto shallow in a pyramid fashion. Then, inspired by the classical bilateralfilter [30], we propose the Bilateral Attention layer (BA-layer) to optimizefilled feature map, which synthesizes feature patches at each position bycomputing weighted sums of the surrounding feature patches, these weights arederived by considering both distance and value relationships between featurepatches, thus making the visually plausible inpainting results. Finally,experiments on multiple publicly available datasets show the superiorperformance of our approach.

One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block