  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.
