Kavli Affiliate: Xiang Zhang
| First 5 Authors: Chi Zhang, Chi Zhang, , ,
| Summary:
Frame-based cameras with extended exposure times often produce perceptible
visual blurring and information loss between frames, significantly degrading
video quality. To address this challenge, we introduce EVDI++, a unified
self-supervised framework for Event-based Video Deblurring and Interpolation
that leverages the high temporal resolution of event cameras to mitigate motion
blur and enable intermediate frame prediction. Specifically, the Learnable
Double Integral (LDI) network is designed to estimate the mapping relation
between reference frames and sharp latent images. Then, we refine the coarse
results and optimize overall training efficiency by introducing a
learning-based division reconstruction module, enabling images to be converted
with varying exposure intervals. We devise an adaptive parameter-free fusion
strategy to obtain the final results, utilizing the confidence embedded in the
LDI outputs of concurrent events. A self-supervised learning framework is
proposed to enable network training with real-world blurry videos and events by
exploring the mutual constraints among blurry frames, latent images, and event
streams. We further construct a dataset with real-world blurry images and
events using a DAVIS346c camera, demonstrating the generalizability of the
proposed EVDI++ in real-world scenarios. Extensive experiments on both
synthetic and real-world datasets show that our method achieves
state-of-the-art performance in video deblurring and interpolation tasks.
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