一个轻量级的重复分组注意力网络,用于视频超分辨率.
1School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种轻量级的反复分组注意力网络,用于视频超分辨率. 新型模型有效地汇总时间信息,以显著减少参数实现最先进的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视频超分辨率 (VSR) 模型需要有效的时间信息聚合.
- 当前的VSR模型通常具有较大的参数数量,增加了硬件需求.
研究的目的:
- 为VSR.提出一个新的,轻量级的循环分组注意力网络.
- 为了减少VSR模型的计算负担和参数大小.
主要方法:
- 设计了前向和后向特征提取模块,用于双向时间信息收集.
- 引入了一个集群机制,以有效地收集时空信息.
- 使用注意力补充模块来扩大信息收集范围.
主要成果:
- 拟议的模型只有0.878M的参数,明显低于主流VSR模型.
- 在多个基准数据集上实现了最先进的性能.
- 证明了时间和时空信息的有效聚合.
结论:
- 轻量级的反复分组注意网络为VSR提供了一个计算效率高的解决方案.
- 该模型的架构有效地捕捉了高分辨率视频重建所需的时空细节.
- 这种方法平衡了性能与减少资源需求.
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