While real-world anime super-resolution (SR) has gained increasing attention in the SR community, existing methods still adopt techniques from the photorealistic domain. In this paper, we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR. First, we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead, we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline, we introduce the Anime Production-oriented Image (API) dataset. In addition, we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines. In addition, we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark, showing our method outperforms state-of-the-art anime dataset-trained approaches.

翻译：尽管真实世界动漫超分辨率在超分辨率领域日益受到关注，现有方法仍沿用源自照片写实域的技术。本文通过分析动漫制作流程，重新思考如何利用其特性解决真实世界动漫超分辨率问题。首先，我们论证由于手绘帧的重复使用，视频网络与数据集对动漫超分辨率并非必要。为此，我们提出一种从视频源中选择压缩程度最低且信息量最丰富的帧的动漫图像采集流程，并基于该流程构建了面向动漫制作的图像数据集（API）。此外，我们识别出两个动漫特有挑战：手绘线条的扭曲模糊与伪影色斑。针对前者，我们在图像退化模型中引入面向预测的压缩模块，并制备具有增强手绘线条的伪真值；针对后者，我们提出平衡型双感知损失函数，融合动漫与照片写实域高层特征以减轻色伪影并提升视觉清晰度。通过在公开基准上的大量实验证明，本方法优于现有基于动漫数据集训练的先进技术。