Image Inpainting with Onion Convolutions

Recently deep learning methods have achieved a great success in image inpainting problem. However, reconstructing continuities of complex structures with non-stationary textures remains a challenging task for computer vision. In this paper, a novel approach to image inpainting problem is presented, which adapts exemplar-based methods for deep convolutional neural networks. The concept of onion convolution is introduced with the purpose of preserving feature continuities and semantic coherence. Similar to recent approaches, our onion convolution is able to capture long-range spatial correlations. In general, the implementation of modules with such ability in low-level features leads to impractically high latency and complexity. To address this limitations, the onion convolution suggests an efficient implementation. As qualitative and quantitative comparisons show, our method with onion convolutions outperforms state-of-the-art methods by producing more realistic, visually plausible and semantically coherent results.