Abstract
In this paper, we tackle the task of blurry video super-resolution (BVSR), aiming to generate high-resolution (HR) videos from low-resolution (LR) and blurry inputs. Current BVSR methods often fail to restore sharp details at high resolutions, resulting in noticeable artifacts and jitter due to insufficient motion information for deconvolution and the lack of high-frequency details in LR frames. To address these challenges, we introduce event signals into BVSR and propose a novel event-enhanced network, Ev-DeblurVSR. To effectively fuse information from frames and events for feature deblurring, we introduce a reciprocal feature deblurring module that leverages motion information from intra-frame events to deblur frame features while reciprocally using global scene context from the frames to enhance event features. Furthermore, to enhance temporal consistency, we propose a hybrid deformable alignment module that fully exploits the complementary motion information from inter-frame events and optical flow to improve motion estimation in the deformable alignment process. Extensive evaluations demonstrate that Ev-DeblurVSR establishes a new state-of-the-art performance on both synthetic and real-world datasets. Notably, on real data, our method is +2.59 dB more accurate and 7.28$\times$ faster than the recent best BVSR baseline FMA-Net. Code: this https URL.
Abstract (translated)
在这篇论文中，我们处理的是模糊视频超分辨率（BVSR）任务，目标是从低分辨率（LR）和模糊的输入生成高分辨率（HR）视频。当前的BVSR方法通常无法在高分辨率下恢复出清晰细节，导致由于解卷积所需的运动信息不足以及低分辨率帧缺乏高频细节而产生明显的伪影和抖动问题。为了解决这些挑战，我们引入了事件信号到BVSR中，并提出了一种新的基于事件增强网络Ev-DeblurVSR。为了有效地融合来自帧和事件的信息以进行特征去模糊化处理，我们设计了一个互反特征去模糊模块，该模块利用帧内事件的运动信息来对帧特征进行去模糊化处理，同时又利用全局场景上下文从帧中提升事件特征的质量。此外，为增强时间一致性，我们提出了一种混合可变形对齐模块，它充分利用了来自帧间事件和光流的互补运动信息，从而改进了可变形对齐过程中的运动估计。 广泛的评估表明，Ev-DeblurVSR在合成数据集和真实世界数据集中都建立了新的最先进的性能。值得注意的是，在真实数据上，我们的方法比最近最好的BVSR基准FMA-Net更准确+2.59 dB且速度提高了7.28倍。代码：[请参考相关链接] (此链接处应填入实际的代码链接地址)
URL
https://arxiv.org/abs/2504.13042
