A Real-World Animation Super-Resolution Benchmark With Color Degradation and Multi-Scale Multi-Frequency Alignment

Abstract

Animation super-resolution (SR) aims to generate high-resolution (HR) animation frames from degraded low-resolution (LR) inputs, constituting an important task in real-world SR. Existing animation SR methods typically follow a photorealistic real-world SR computational paradigm. However, digital animation frames commonly suffer from compression and transmission-related degradation, distinct from degradations in camera-captured real-world images. In this paper, we introduce a novel real-world animation super-resolution benchmark designed explicitly for animation frames, named ADASR, featuring both 2D and modern 3D animation content to facilitate industry applications. Additionally, we propose a Color-Aware Animation Super-Resolution (CAASR) method. CAASR, for the first time, incorporates a color degradation simulation mechanism tailored for animations, addressing color banding, blocking, and color shift. Furthermore, we develop a multi-scale multi-frequency alignment mechanism to robustly extract degradation-invariant features. Extensive experiments conducted on both the existing AVC dataset and our newly constructed ADASR dataset demonstrate that our proposed CAASR achieves state-of-the-art performance in restoring HR frames for both 2D and 3D animations. Code and data are available at https://github.com/huangyang-666/CAASR.