  Motion blur is a fundamental problem in computer vision as it impacts image
quality and hinders inference. Traditional deblurring algorithms leverage the
physics of the image formation model and use hand-crafted priors: they usually
produce results that better reflect the underlying scene, but present
artifacts. Recent learning-based methods implicitly extract the distribution of
natural images directly from the data and use it to synthesize plausible
images. Their results are impressive, but they are not always faithful to the
content of the latent image. We present an approach that bridges the two. Our
method fine-tunes existing deblurring neural networks in a self-supervised
fashion by enforcing that the output, when blurred based on the optical flow
between subsequent frames, matches the input blurry image. We show that our
method significantly improves the performance of existing methods on several
datasets both visually and in terms of image quality metrics. The supplementary
material is https://goo.gl/nYPjEQ
