DAVIDE: Depth-Aware Video Deblurring

TL;DR

This paper introduces DAVIDE, a new dataset and method demonstrating that depth information can significantly improve video deblurring, especially with limited temporal context, highlighting the importance of depth cues in sharpness recovery.

Contribution

The paper presents a novel dataset and a baseline approach for depth-aware video deblurring, analyzing how depth information enhances deblurring performance in various scenarios.

Findings

Depth information significantly improves deblurring accuracy.

The benefit of depth cues diminishes with longer temporal context.

The dataset enables studying depth's role in video deblurring.

Abstract

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/ .