  We consider the challenging task of training models for image-to-video
deblurring, which aims to recover a sequence of sharp images corresponding to a
given blurry image input. A critical issue disturbing the training of an
image-to-video model is the ambiguity of the frame ordering since both the
forward and backward sequences are plausible solutions. This paper proposes an
effective self-supervised ordering scheme that allows training high-quality
image-to-video deblurring models. Unlike previous methods that rely on
order-invariant losses, we assign an explicit order for each video sequence,
thus avoiding the order-ambiguity issue. Specifically, we map each video
sequence to a vector in a latent high-dimensional space so that there exists a
hyperplane such that for every video sequence, the vectors extracted from it
and its reversed sequence are on different sides of the hyperplane. The side of
the vectors will be used to define the order of the corresponding sequence.
Last but not least, we propose a real-image dataset for the image-to-video
deblurring problem that covers a variety of popular domains, including face,
hand, and street. Extensive experimental results confirm the effectiveness of
our method. Code and data are available at
https://github.com/VinAIResearch/HyperCUT.git
