Animation Video Super Resolution

Animation video super-resolution (SR) aims to enhance the resolution of low-quality animation videos, focusing on overcoming challenges specific to the anime style, such as hand-drawn lines and unique color palettes. Recent research emphasizes learning data-driven degradation models tailored to animation characteristics, often employing neural networks with multi-scale architectures or vector quantization to effectively capture and restore these features. These advancements are improving the quality of upscaled animation videos, impacting both the scientific community through the development of new benchmark datasets and algorithms, and practical applications such as video restoration and enhancement for broadcasting and streaming services.