Name
Title | ||
|---|---|---|
FRAGAN-VSR: Frame-Recurrent Attention Generative Adversarial Network for Video Super-Resolution |
Abstract | ||
|---|---|---|
Video super resolution (SR) is an important task, which recovers high-resolution (HR) frames from consecutive low-resolution (LR) couterparts. The most advanced works achieved good performance to this day. However, most of them has largely focussed on making a breakthrough in accuracy and speed, which has neglect that how to recover the finer texture details. Therefore, in this paper, we first present an Video SR model combined generative adversarial network and recurrent neural network (GAN-RNN) structure. It is forced by the self-attention mechanism to pay great attention to the high-frequency information of the LR frames. The perceptual loss is introduced to retain the high-frequency detail which is different from other video SR network. A great deal of evaluations and comparisons with previous methods have confirmed the merits of the proposed framework which can significantly outperform the current state of the art. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICTAI52525.2021.00119 | 2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021) |
Keywords | DocType | ISSN |
Video Super-Resolution, Generative Adversarial Network, Recurrent Neural Network, Self-attention Mechanism | Conference | 1082-3409 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuzhen Zhang | 1 | 0 | 0.34 |
| Guanqun Liu | 2 | 0 | 0.68 |
| Yanyan Zhao | 3 | 0 | 0.34 |
| Daren Zha | 4 | 16 | 7.85 |
| Xin Wang | 5 | 587 | 177.85 |
| Lin Zhao | 6 | 5 | 1.18 |
| Lei Wang | 7 | 0 | 1.69 |