Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing methods fail to effectively reconstruct vivid contents and refine structures. In order to solve this issue, in this paper, a novel two-stage generative adversarial network based on the fusion of edge structures and color aware maps is proposed. In the first-stage network, edges with missing regions are employed to train an edge structure generator. Meanwhile, the input image with missing regions is transformed into a global color feature map after the content aware fill algorithm and a large kernel size Gaussian filtering. In the second-stage network, the image fused from the edge map and the color map is used as a label to guide the network to reconstruct the refined image. Qualitative and quantitative experiments conducted on multiple public datasets demonstrate that the method proposed in this paper has superior performance.

最近，随着深度学习的广泛研究，其成就在图像修复任务中越来越明显。但是，许多现有方法无法有效地重建生动的内容并细化结构。为了解决这个问题，本文提出了一种基于边缘结构和颜色感知图的融合的新型两阶段生成对抗网络。在第一阶段网络中，采用具有缺失区域的边缘来训练边缘结构生成器。同时，在内容感知填充算法和大内核尺寸高斯滤波之后，将具有缺失区域的输入图像转换为全局色彩特征图。在第二阶段网络中，将从边缘图和颜色图融合的图像用作标签，以引导网络重构精制图像。