提出首个深度感知视频去模糊数据集，提升模糊视频恢复质量。
DAVIDE: Depth-Aware Video Deblurring
- 构建同步模糊、清晰与深度视频数据集，支持深度信息融合研究。
- 深度信息可显著提升去模糊效果，但长时序下增益减弱。
- 为移动设备上利用深度传感器优化视频去模糊提供新思路。
视频去模糊旨在从一系列模糊帧中恢复清晰细节。尽管深度传感器在手机中日益普及，且深度信息有望指导去模糊，但深度感知去模糊研究仍较少。本文提出‘深度感知视频去模糊’（DAVIDE）数据集，用于研究深度信息在视频去模糊中的作用。该数据集包含同步的模糊、清晰与深度视频序列。我们探讨了如何将深度信息融入现有基于RGB的视频去模糊深度模型，并提出一种强基线方法。结果表明，深度信息对去模糊至关重要，且在某些场景下尤为有效。此外，实验显示，当模型获得更长的时间上下文时，深度带来的性能提升会减弱。项目主页：https://germanftv.github.io/DAVIDE.github.io/
原文摘要 · Abstract (English)
Video deblurring aims at recovering sharp details from a sequence of blurry frames. Despite the proliferation of depth sensors in mobile phones and the potential of depth information to guide deblurring, depth-aware deblurring has received only limited attention. In this work, we introduce the 'Depth-Aware VIdeo DEblurring' (DAVIDE) dataset to study the impact of depth information in video deblurring. The dataset comprises synchronized blurred, sharp, and depth videos. We investigate how the depth information should be injected into the existing deep RGB video deblurring models, and propose a strong baseline for depth-aware video deblurring. Our findings reveal the significance of depth information in video deblurring and provide insights into the use cases where depth cues are beneficial. In addition, our results demonstrate that while the depth improves deblurring performance, this effect diminishes when models are provided with a longer temporal context. Project page: https://germanftv.github.io/DAVIDE.github.io/ .
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