论文标题
STDAN：时空视频超分辨率的可变形注意网络
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
论文作者
论文摘要
时空视频超分辨率（STVSR）的目标是增加低分辨率（LR）和低帧速率（LFR）视频的空间分辨率。基于深度学习的最新方法已取得了重大改进，但是其中大多数仅使用两个相邻框架，即短期功能，可以合成缺失的框架嵌入，这无法完全探索连续输入LR帧的信息流。此外，现有的STVSR模型几乎无法明确利用时间上下文以帮助高分辨率（HR）框架重建。为了解决这些问题，在本文中，我们提出了一个名为STDAN的可变形注意网络，用于STVSR。首先，我们设计了一个长短的术语特征插值（LSTFI）模块，该模块能够通过双向RNN结构从更多相邻的输入帧中挖掘更多相邻输入帧的丰富内容。其次，我们提出了一个空间变形的特征聚合（STDFA）模块，其中动态视频框架中的空间和时间上下文被自适应捕获并汇总以增强SR重建。几个数据集的实验结果表明，我们的方法的表现优于最先进的STVSR方法。该代码可在https://github.com/littlewhitesea/stdan上找到。
The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low frame rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synthesize the missing frame embedding, which cannot fully explore the information flow of consecutive input LR frames. In addition, existing STVSR models hardly exploit the temporal contexts explicitly to assist high-resolution (HR) frame reconstruction. To address these issues, in this paper, we propose a deformable attention network called STDAN for STVSR. First, we devise a long-short term feature interpolation (LSTFI) module, which is capable of excavating abundant content from more neighboring input frames for the interpolation process through a bidirectional RNN structure. Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction. Experimental results on several datasets demonstrate that our approach outperforms state-of-the-art STVSR methods. The code is available at https://github.com/littlewhitesea/STDAN.
