Animesr: learnable degradation operator and new real world animation VSR dataset

Xintao Another masterpiece of the big guy team , This paper 『AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos』 For real animation VSR A new animation data set is proposed , In addition, the real-world degradation operator is extended to a learnable operator , stay NIQE And other evaluation indicators SOTA.

• Author's unit ： tencent PCG ARC laboratory

• Thesis link ：https://arxiv.org/pdf/2206.07038

This article summarizes three implementation of animation VSR Three key improvement measures ：

1. The recent real world VSR The degradation of methods mostly uses basic operators without learning ability , As fuzzy 、 Noise and compression . This article suggests starting from the real LQ Learn these basic operators in animation , And the learned operators are added to the degradation process . This basic operation based on neural network can help to better capture the distribution of real degradation .

2. Established a large-scale HQ Animation dataset AVC, So that the animation VSR Train and evaluate .

3. An efficient multiscale network structure is studied AnimeSR, It makes use of the efficiency of one-way loop network and the effectiveness of sliding window method , Achieve better performance than previous advanced methods .