论文标题
指导和评估：混合场景的语义感知图像介绍
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
论文作者
论文摘要
用正确的结构和混合场景的合理纹理完成损坏的图像仍然是一个难以捉摸的挑战。由于损坏图像混合场景中的缺失孔通常包含各种语义信息，因此使用结构信息的传统两阶段方法通常会导致不可靠的结构预测和模棱两可的图像纹理生成问题。在本文中，我们提出了一个语义指导和评估网络（SGE-net），以迭代地更新结构先验和在语义提取和图像插入图像框架的框架中的结构先验和对图像。它利用语义分割图作为每个范围内的指导，根据该指导，根据位置依赖性的推论，对此进行了重新评估，因此，在随后的尺度中，提出了不良的区域。关于混合场景的现实世界图像的广泛实验证明了我们所提出的方法比最先进的方法在清晰的边界和光真逼真的纹理方面具有优越性。
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.
