发表机构
Lomonosov Moscow State University; MSU Institute for Artificial Intelligence（莫斯科国立罗蒙诺索夫大学; 莫斯科国立大学人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对手持设备视频运动模糊，提出TSRN-RTVD系统，通过重建曝光期间相机轨迹引导去模糊，在消费级GPU上实现30 FPS实时处理，GoPro数据集PSNR达30.08 dB。
AI 中文摘要
随着视频拍摄转向手持和边缘设备，相机抖动引起的运动模糊已成为一种普遍存在的退化现象，它降低了感知质量并损害了下游视觉任务。最强的去模糊网络能够恢复令人印象深刻的细节，但它们仍然计算量大且极其复杂，因此其质量所付出的代价是消费级硬件无法实时承担的。恢复质量与设备端速度之间的这种差距正是实时去模糊困难的原因。我们开发并实现了TSRN-RTVD，一种高效的视频去模糊系统，该系统在曝光期间显式重建底层相机轨迹，并利用恢复的运动来引导恢复。这种方法将模糊的物理原因转化为驱动锐化的信号。我们的系统在单个消费级GPU上运行，以30 FPS恢复视频，同时在GoPro数据集上达到30.08 dB的PSNR。我们在消费设备上演示了TSRN-RTVD，具有模糊输入和去模糊输出的交互式并排可视化、实时吞吐量以及恢复相机轨迹的屏幕视图。演示视频可在https URL获取。
英文摘要
As video capture moves to handheld and edge devices, motion blur from camera shake has become a pervasive degradation that lowers perceptual quality and harms downstream vision tasks. The strongest deblurring networks recover impressive detail, yet they remain computationally heavy and overwhelmingly complex, so their quality comes at a cost that consumer hardware cannot pay in real time. This gap between restoration quality and on-device speed is exactly what makes real-time deblurring difficult. We developed and implemented TSRN-RTVD, an efficient video deblurring system that explicitly reconstructs the underlying camera trajectory during exposure and uses the recovered motion to guide restoration. This approach turns the physical cause of blur into a signal that drives sharpening. Our system runs on a single consumer GPU and restores the video at 30 FPS while reaching 30.08 dB PSNR on the GoPro dataset. We demonstrate TSRN-RTVD on consumer devices with interactive side-by-side visualization of the blurry input and the deblurred output, live throughput, and an on-screen view of the recovered camera trajectory. Demo video is available at https://youtu.be/3alMwVrVALU.
