Generative image inpainting with residual attention learning

Abstract

Recent deep learning approaches have shown significant improvements in the challenging task of image inpainting. However, these methods may generate blurry output and distorted textures. In this paper, we propose an efficient end-to-end two-stage network for image inpainting. In the coarse stage, we employ residual dense block (RDB) as well as short and long skip connections to fully leverage and exploit features from all convolutional layers and give a globally rough reconstruction. In the refinement stage, we propose a local and global residual network based on channel and spatial attention block (CSAB) to adaptively weigh both channel-wise and spatial-wise features focusing on more meaningful information, and generate a locally fine-detailed image. Experiments on Paris StreetView and DTD textures demonstrate the effectiveness and efficiency of our method. Results show that our method outperforms the baseline techniques quantitatively and qualitatively.