  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.
