In this paper, we propose a generative multi-column network for image inpainting.

In this paper, we propose a generative multi-column network for image inpainting.

Summary This submission tackles the problem of image inpainting. It adapts the idea of multi-scale predictions by running three branches that predict features at different scales which are concatenated before two convolutions create the final prediction. The loss consists of three components. The method is evaluated on five diverse datasets and with several ablation studies analyzing the influence of different parts. Strengths - The ablation study shows the contribution of the different components well.

In this paper, we propose a generative multi-column network for image inpainting. To better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing. Papers published at the Neural Information Processing Systems Conference.

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 better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing.

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 better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing.