Abstract
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