Deep learning has been widely applied into image inpainting. However, traditional image processing methods (i.e., patch-based and diffusion-based methods) generally fail to produce visually natural contents and semantically reasonable structures due to ineffectively processing the high-level semantic information of images. To solve the problem, we propose a stacked generator networks assisted by patch discriminator for image inpainting by multistage. In the proposed method, our generator network mainly consists of three-layer stacked encoder-decoder architecture, which could fuse different level feature information and achieve image inpainting via a coarse-to-fine hierarchical representation. Meanwhile, we split the masked image into different patches in each layer, which could effectively enlarge the receptive field and extract more useful features of images. Moreover, the patch discriminator is introduced to judge the patches of inpainting image are real or fake. In this way, our network can effectively utilize the semantic information to complete a fine result. Furthermore, both perceptual loss and style loss are used to improve the inpainting results in verse. Experimental results on Places2 and Paris StreetView illustrate that our approach could generate high-quality inpainting results, and our method is more effective than the existing image inpainting methods.

中文翻译：

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使用堆叠生成器网络进行多阶段语义感知图像修复

深度学习已广泛应用于图像修复。然而，传统的图像处理方法（即基于补丁和基于扩散的方法）由于对图像高级语义信息的处理效率低下，通常无法产生视觉上自然的内容和语义上合理的结构。为了解决这个问题，我们提出了一个由补丁鉴别器辅助的堆叠生成器网络，用于多级图像修复。在所提出的方法中，我们的生成器网络主要由三层堆叠的编码器-解码器架构组成，它可以融合不同级别的特征信息，并通过从粗到细的分层表示实现图像修复。同时，我们在每一层将蒙版图像分成不同的块，可以有效地扩大感受野，提取更多有用的图像特征。此外，引入补丁鉴别器来判断修复图像的补丁是真的还是假的。这样，我们的网络就可以有效地利用语义信息来完成一个精细的结果。此外，感知损失和风格损失都用于改善诗歌中的修复结果。Places2 和巴黎街景的实验结果表明，我们的方法可以产生高质量的修复结果，并且我们的方法比现有的图像修复方法更有效。感知损失和风格损失都用于改善诗歌中的修复结果。Places2 和巴黎街景的实验结果表明，我们的方法可以产生高质量的修复结果，并且我们的方法比现有的图像修复方法更有效。感知损失和风格损失都用于改善诗歌中的修复结果。Places2 和巴黎街景的实验结果表明，我们的方法可以产生高质量的修复结果，并且我们的方法比现有的图像修复方法更有效。