  Recently deep neutral networks have achieved promising performance for
filling large missing regions in image inpainting tasks. They usually adopted
the standard convolutional architecture over the corrupted image, leading to
meaningless contents, such as color discrepancy, blur and artifacts. Moreover,
most inpainting approaches cannot well handle the large continuous missing area
cases. To address these problems, we propose a generic inpainting framework
capable of handling with incomplete images on both continuous and discontinuous
large missing areas, in an adversarial manner. From which, region-wise
convolution is deployed in both generator and discriminator to separately
handle with the different regions, namely existing regions and missing ones.
Moreover, a correlation loss is introduced to capture the non-local
correlations between different patches, and thus guides the generator to obtain
more information during inference. With the help of our proposed framework, we
can restore semantically reasonable and visually realistic images. Extensive
experiments on three widely-used datasets for image inpainting tasks have been
conducted, and both qualitative and quantitative experimental results
demonstrate that the proposed model significantly outperforms the
state-of-the-art approaches, both on the large continuous and discontinuous
missing areas.
