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. Our code is available at this https URL

本论文提出一种名为 “Region Normalization” 的空间区域归一化方法，能够解决全空间特征归一化带来的影响，即均值和方差的偏移，特别是在图像修复领域中。该方法在网络的不同层次应用了两种不同的区域归一化策略，成功提升了修复结果的质量和准确性，并被证明是一种可行的通用方法。

图像修复的区域归一化

Region Normalization for Image Inpainting

Image inpainting is a challenging problem as it needs to fill the information
of the corrupted regions. Most of the existing inpainting algorithms assume
that the positions of the corrupted regions are known. Different from the
existing methods that usually make some assumptions on the corrupted regions,
we present an efficient blind image inpainting algorithm to directly restore a
clear image from a corrupted input. Our algorithm is motivated by the residual
learning algorithm which aims to learn the missing infor- mation in corrupted
regions. However, directly using exist- ing residual learning algorithms in
image restoration does not well solve this problem as little information is
available in the corrupted regions. To solve this problem, we introduce an
encoder and decoder architecture to capture more useful information and develop
a robust loss function to deal with outliers. Our algorithm can predict the
missing information in the corrupted regions, thus facilitating the clear image
restoration. Both qualitative and quantitative experimental demonstrate that
our algorithm can deal with the corrupted regions of arbitrary shapes and
performs favorably against state-of-the-art methods.

提出了一种盲图像修复算法，通过利用残差学习与编码解码结构来恢复缺失信息，以处理任意形状的损坏区域。