  We present a method to extract a video sequence from a single motion-blurred
image. Motion-blurred images are the result of an averaging process, where
instant frames are accumulated over time during the exposure of the sensor.
Unfortunately, reversing this process is nontrivial. Firstly, averaging
destroys the temporal ordering of the frames. Secondly, the recovery of a
single frame is a blind deconvolution task, which is highly ill-posed. We
present a deep learning scheme that gradually reconstructs a temporal ordering
by sequentially extracting pairs of frames. Our main contribution is to
introduce loss functions invariant to the temporal order. This lets a neural
network choose during training what frame to output among the possible
combinations. We also address the ill-posedness of deblurring by designing a
network with a large receptive field and implemented via resampling to achieve
a higher computational efficiency. Our proposed method can successfully
retrieve sharp image sequences from a single motion blurred image and can
generalize well on synthetic and real datasets captured with different cameras.
