Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.

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a) Ground Truth (b) Damaged image (c) Once dilation (d) Twice dilation (e) Final recovering Figure 1: Progressive image inpainting for irregular holes. ABSTRACTRecently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes. However, they fail to generate plausible textures inside damaged area because there lacks surrounding information. A progressive inpainting approach would be advantageous for eliminating central blurriness, i.e., restoring well and then updating masks. In this paper, we propose full-resolution residual network (FRRN) to fill irregular holes, which is proved to be effective for progressive image inpainting. We show that well-designed residual architecture facilitates feature integration and texture prediction. Additionally, to guarantee completion quality during progressive inpainting, we adopt N Blocks, One Dilation strategy, which assigns several residual blocks for one dilation step. Correspondingly, a step loss function is applied to improve the performance of intermediate restorations. The experimental results demonstrate that the proposed FRRN framework for image inpainting is much better than previous methods both quantitatively and qualitatively. Our codes are released at: https://github.com/ZongyuGuo/Inpainting_FRRN. CCS CONCEPTS• Applied computing → Fine arts. KEYWORDSprogressive image inpainting; irregular mask; residual network; computer vision ACM Reference Format:

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