DAVIDE: Depth-Aware Video Deblurring
This work addresses video deblurring for computer vision applications, but it is incremental as it builds on existing deep RGB models and focuses on dataset creation and baseline analysis.
The paper tackled video deblurring by introducing the DAVIDE dataset with synchronized blurred, sharp, and depth videos, and found that depth information improves deblurring performance, though the effect diminishes with longer temporal context.
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/ .