Recently data-driven image inpainting methods have made inspiring progress, impacting
fundamental image editing tasks such as object removal and damaged image repairing …

We study the task of image inpainting, where an image with missing region is recovered with
plausible context. Recent approaches based on deep neural networks have exhibited …

Among the various generative adversarial network (GAN)-based image inpainting methods,
a coarse-to-fine network with a contextual attention module (CAM) has shown remarkable …

Recently, image inpainting task has revived with the help of deep learning techniques. Deep
neural networks, especially the generative adversarial networks~(GANs) make it possible to …

In this paper, we propose a generative multi-column network for image inpainting. This
network synthesizes different image components in a parallel manner within one stage. To …

Deep image inpainting can inpaint a corrupted image using a feed-forward inference, but
still fails to handle large missing area or complex semantics. Recently, GAN inversion based …

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

The purpose of image inpainting is to recover scratches and damaged areas using context
information from remaining parts. In recent years, with the development of convolutional …

Recent advances in image inpainting have shown impressive results for generating
plausible visual details on rather simple backgrounds. However, for complex scenes, it is still …

Although recent inpainting approaches have demonstrated significant improvement with
deep neural networks, they still suffer from artifacts such as blunt structures and abrupt …