Learning Parallax for Stereo Event-based Motion Deblurring

TL;DR

This paper introduces St-EDNet, a novel framework that uses stereo event and intensity cameras to improve motion deblurring by handling misaligned inputs without requiring ground-truth depth.

Contribution

It proposes a coarse-to-fine stereo matching and dual-feature embedding approach for high-quality image reconstruction from misaligned event and intensity data.

Findings

Outperforms state-of-the-art methods on real-world datasets.

Effectively handles misaligned inputs without ground-truth depths.

Introduces a new stereo event and intensity dataset with dense disparity maps.

Abstract

Due to the extremely low latency, events have been recently exploited to supplement lost information for motion deblurring. Existing approaches largely rely on the perfect pixel-wise alignment between intensity images and events, which is not always fulfilled in the real world. To tackle this problem, we propose a novel coarse-to-fine framework, named NETwork of Event-based motion Deblurring with STereo event and intensity cameras (St-EDNet), to recover high-quality images directly from the misaligned inputs, consisting of a single blurry image and the concurrent event streams. Specifically, the coarse spatial alignment of the blurry image and the event streams is first implemented with a cross-modal stereo matching module without the need for ground-truth depths. Then, a dual-feature embedding architecture is proposed to gradually build the fine bidirectional association of the coarsely aligned data and reconstruct the sequence of the latent sharp images. Furthermore, we build a new dataset with STereo Event and Intensity Cameras (StEIC), containing real-world events, intensity images, and dense disparity maps. Experiments on real-world datasets demonstrate the superiority of the proposed network over state-of-the-art methods.