Deep Image Inpainting With Enhanced Normalization and Contextual Attention

Deep learning-based image inpainting has been widely studied, leading to great success. However, many methods adopt convolution and normalization operations, which will bring up some issues to affect the performance. The vanilla normalization cannot distinguish the pixels in corrupted regions from the other valid pixels, resulting in the mean and variance shifts. In addition, the limited receptive field of convolution makes it unable to capture long-range valid information directly. In order to tackle these challenges, we propose a novel deep generative model for image inpainting with two key modules, namely, the channel and spatially adaptive batch normalization (CSA-BN) module, and the selective latent-space-mapping-based contextual attention (SLSM-CA) layer. We replace the vanilla normalization with the CSA-BN module. By channel and spatially adaptive denormalization, the CSA-BN module can mitigate the spatial mean and variance shifts in each channel in a targeted way. In addition, we also integrate the SLSM-CA layer into our model to capture the long-range correlations explicitly. By introducing dual-branch attention and a feature selection module, the SLSM-CA layer can selectively utilize the multi-scale background information to improve prediction quality. What's more, it introduces the latent spaces to achieve the low-rank approximations of attention matrices and to reduce computational costs. Extensive quantitative and qualitative evaluations demonstrate the superiority of the proposed method compared with state-of-the-art methods.