  This paper aims at demystifying a single motion-blurred image with events and
revealing temporally continuous scene dynamics encrypted behind motion blurs.
To achieve this end, an Implicit Video Function (IVF) is learned to represent a
single motion blurred image with concurrent events, enabling the latent sharp
image restoration of arbitrary timestamps in the range of imaging exposures.
Specifically, a dual attention transformer is proposed to efficiently leverage
merits from both modalities, i.e., the high temporal resolution of event
features and the smoothness of image features, alleviating temporal ambiguities
while suppressing the event noise. The proposed network is trained only with
the supervision of ground-truth images of limited referenced timestamps.
Motion- and texture-guided supervisions are employed simultaneously to enhance
restorations of the non-referenced timestamps and improve the overall
sharpness. Experiments on synthetic, semi-synthetic, and real-world datasets
demonstrate that our proposed method outperforms state-of-the-art methods by a
large margin in terms of both objective PSNR and SSIM measurements and
subjective evaluations.
