AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos

Understand

This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR.

• First, recent real-world super-resolution approaches typically rely on degradation simulation using basic operators without any learning capability, such as blur, noise, and compression.
• In this work, we propose to learn such basic operators from real low-quality animation videos, and incorporate the learned ones into the degradation generation pipeline.
• Such neural-network-based basic operators could help to better capture the distribution of real degradations.