论文标题
快速在线视频超分辨率，可变形金字塔
Fast Online Video Super-Resolution with Deformable Attention Pyramid
论文作者
论文摘要
视频超分辨率（VSR）具有许多构成严格因果，实时和延迟限制的应用程序，包括视频流和电视。我们在这些设置下解决了VSR问题，这提出了其他重要挑战，因为未来框架的信息不可用。重要的是，设计有效但有效的框架对准和融合模块仍然是核心问题。在这项工作中，我们提出了基于可变形的金字塔（DAP）的复发性VSR架构。我们的DAP对齐并将从复发状态的信息集成到当前框架预测中。为了避免传统基于注意力的方法的计算成本，我们只关注有限数量的空间位置，这些空间位置是由DAP动态预测的。对拟议的关键创新的全面实验和分析表明了我们方法的有效性。与最先进的方法相比，我们显着降低了处理时间和计算复杂性，同时保持高性能。我们在两个标准基准测试上超过$ 3 \ times $的标准基准上超过了最新的方法EDVR-M。
Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We address the VSR problem under these settings, which poses additional important challenges since information from future frames is unavailable. Importantly, designing efficient, yet effective frame alignment and fusion modules remain central problems. In this work, we propose a recurrent VSR architecture based on a deformable attention pyramid (DAP). Our DAP aligns and integrates information from the recurrent state into the current frame prediction. To circumvent the computational cost of traditional attention-based methods, we only attend to a limited number of spatial locations, which are dynamically predicted by the DAP. Comprehensive experiments and analysis of the proposed key innovations show the effectiveness of our approach. We significantly reduce processing time and computational complexity in comparison to state-of-the-art methods, while maintaining a high performance. We surpass state-of-the-art method EDVR-M on two standard benchmarks with a speed-up of over $3\times$.
