Abstract: To overcome the shortcomings of existing image inpainting methods, such as the lack of authenticity in the inpainting results, the lack of effective processing of missing region and non-missing region information, and the lack of effective processing of image feature information in different stages, an image inpainting method combining semantic priors and deep attention residual group is proposed. The image inpainting network is mainly composed of semantic priors network, deep attention residual group and full-scale skip connection. The semantic priors network learns the complete semantic priors information of visual elements in the missing region, and uses the learned semantic information to complete the missing region. The deep attention residual group enables the generator not only to pay more attention to the missing area of the image, but also to learn the features of each channel adaptively. The full-scale skip connection can combine the low-level feature map containing the image boundary with the high-level feature map containing the image texture and detail to inpaint the missing area of the image. In this paper, a full comparison experiment is conducted on CelebA-HQ dataset and Paris Street View dataset, and the experimental results show that the proposed method is superior to the current representative advanced image inpainting methods.

Key words: image inpainting, encoder and decoder, semantic priors, attention mechanism, skip connection

摘要： 针对现有图像修复方法修复结果缺乏真实性、未灵活处理缺失区域和未缺失区域信息以及未有效处理不同阶段的图像特征信息等问题，提出结合语义先验和深度注意力残差组的图像修复方法。该图像修复方法主要由语义先验网络、深度注意力残差组与全尺度跳跃连接组成。语义先验网络学习缺失区域视觉元素的完整语义先验信息，利用学习到的语义信息对缺失区域进行补全。深度注意力残差组使生成器不仅能更加关注图像的缺失区域，而且能自适应地学习各个通道的特征。全尺度跳跃连接则可以将包含图像边界的低层次特征图与包含图像纹理与细节的高层次特征图结合起来对图像缺失区域进行修复。在CelebA-HQ数据集与Paris Street View数据集上进行了充分对比实验，实验结果表明，该方法优于当前代表性先进图像修复方法。

关键词: 图像修复, 编码解码, 语义先验, 注意力机制, 跳跃连接