Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulfilled, owing to difficulties caused by large-scale area loss and less legitimate neighboring information. These difficulties have made outpainted images handled by most of the existing models unrealistic to human eyes and spatially inconsistent. When upsampling through deconvolution to generate fake content, the naive generation methods may lead to results lacking high-frequency details and structural authenticity. Therefore, as our novelties to handle image outpainting problems, we introduce structural prior as a condition to optimize the generation quality and a new semantic embedding term to enhance perceptual sanity. we propose a deep learning method based on Generative Adversarial Network (GAN) and condition edges as structural prior in order to assist the generation. We use a multi-phase adversarial training scheme that comprises edge inference training, contents inpainting training, and joint training. The newly added semantic embedding loss is proved effective in practice.
翻译:在过去几年里,深层学习技术在图像油漆、恢复和重建方面取得了相当大的进展。图像油漆(又称图像外推)缺乏关注和实际的实现方法,因为大规模地区损失和不太合法的相邻信息造成的困难。这些困难使得大多数现有模型处理的外涂图像对人类眼睛不切实际,空间上不一致。当通过分解抽取以生成假内容时,幼稚的生成方法可能导致缺乏高频细节和结构真实性的结果。因此,作为我们处理图像外推问题的新手,我们先引入结构结构,作为优化生成质量的条件,并引入新的语义嵌入术语,以加强感性。我们提出了一种深层次的学习方法,其基础是基因对立网络(GAN)和条件边缘作为结构的先期,以便帮助下一代。我们使用了多阶段的对抗性培训计划,其中包括精锐的推断培训、修补培训内容和联合培训。新增加的语义嵌入损失在实践中证明是有效的。