Completing a corrupted image with correct structures and reasonable textures
for a mixed scene remains an elusive challenge. Since the missing hole in a
mixed scene of a corrupted image often contains various semantic information,
conventional two-stage approaches utilizing structural information often lead
to the problem of unreliable structural prediction and ambiguous image texture
generation. In this paper, we propose a Semantic Guidance and Evaluation
Network (SGE-Net) to iteratively update the structural priors and the inpainted
image in an interplay framework of semantics extraction and image inpainting.
It utilizes semantic segmentation map as guidance in each scale of inpainting,
under which location-dependent inferences are re-evaluated, and, accordingly,
poorly-inferred regions are refined in subsequent scales. Extensive experiments
on real-world images of mixed scenes demonstrated the superiority of our
proposed method over state-of-the-art approaches, in terms of clear boundaries
and photo-realistic textures.

本研究提出了一种基于语义引导和评估网络的图像修复算法，该算法在每个缩放级别利用语义分割图指导图像修复过程，并利用迭代更新方法实现结构先验和图像修复的协同更新，实验结果表明该方法在图像纹理清晰度和真实感等方面具有优越性。