计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 221100156-10.doi: 10.11896/jsjkx.221100156
王宇骥1, 董昊呈1, 龚雪鸾2, 陈艳姣3
WANG Yuji1, DONG Haocheng1, GONG Xueluan2, CHEN Yanjiao3
摘要: 为了解决视频超分辨率的问题,可以对视频中的时空相关性信息加以利用,这是将低分辨率视频重建为高分辨率视频的一种行之有效的方法。之前的相关工作主要集中在利用运动补偿来捕捉视频生成中的时间依赖性,这种阶段性重建策略是低效的。相比运动补偿,注意力模型更能在寻找时空相关性中发挥作用。为了使注意力模型可以被应用于视频超分辨率问题,利用基于摊销变分推理的注意力估计构建潜在注意力模型,并设计了长程注意力模块和短程注意力模块两个有效的注意力功能模块。在此基础上构建出一个新型深度学习网络模型,它可以有效地捕捉视频超分辨率的时空相关性,并允许端到端学习。通过在公共视频数据集的广泛实验,可以证明该方法相比当前最先进的几种方法如SPMC,DUF-16L等具有更优越的性能。
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