Existing inpainting methods have achieved promising performance for
recovering regular or small image defects. However, filling in large continuous
holes remains difficult due to the lack of constraints for the hole center. In
this paper, we devise a Recurrent Feature Reasoning (RFR) network which is
mainly constructed by a plug-and-play Recurrent Feature Reasoning module and a
Knowledge Consistent Attention (KCA) module. Analogous to how humans solve
puzzles (i.e., first solve the easier parts and then use the results as
additional information to solve difficult parts), the RFR module recurrently
infers the hole boundaries of the convolutional feature maps and then uses them
as clues for further inference. The module progressively strengthens the
constraints for the hole center and the results become explicit. To capture
information from distant places in the feature map for RFR, we further develop
KCA and incorporate it in RFR. Empirically, we first compare the proposed
RFR-Net with existing backbones, demonstrating that RFR-Net is more efficient
(e.g., a 4\% SSIM improvement for the same model size). We then place the
network in the context of the current state-of-the-art, where it exhibits
improved performance. The corresponding source code is available at:
this https URL

本文提出一种基于 Recurrent Feature Reasoning 和 Knowledge Consistent Attention 的深度学习方法，有效地解决了大空洞修复的问题。模型首先循环推理卷积特征图的孔边界，再将其作为约束条件进行推理以填充孔洞，并且引入了 Attention 机制用于从最远处的特征图位置获取信息。