APISR: Anime Production Inspired Real-World Anime Super-Resolution

Boyang Wang, Fengyu Yang, Xihang Yu, Chao Zhang, Hanbin Zhao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25574-25584

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.