Anime4K: A Hybrid CNN–Transformer Network for Anime Super-Resolution

Although anime super-resolution (SR) has garnered increasing interest in recent years, many existing approaches still inherit design principles from photorealistic imagery, overlooking the unique visual traits of anime. In this work, we revisit the anime SR task from an anime-specific perspective and propose a unified solution tailored for real-world restoration. First, we present A4K, the first 4K-resolution dataset specifically curated for anime SR, built via a dual-criterion selection pipeline combining perceptual quality and structural complexity, resulting in cleaner and more informative training samples. Second, we introduce AniFusionNet, a hybrid CNN–Transformer architecture that dynamically fuses local convolutional features with global self-attention, effectively balancing fine detail reconstruction and global coherence. Finally, we introduce a targeted ground truth (GT) enhancement strategy that selectively strengthens hand-drawn line structures, enabling more accurate learning of anime-specific textures. Extensive experiments on public benchmarks demonstrate that our approach achieves state-of-the-art performance in edge sharpness, color consistency, and artifact suppression. The project is available athttps://github.com/wasai67/Anime4K