From Motion Blur to Motion Flow: a Deep Learning Solution for
Removing Heterogeneous Motion Blur
Abstract
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding
a prior, but extensive literature on the subject indicates the
difficulty in identifying a prior which is suitably informative,
and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling
all possible image content. The critical observation underpinning our approach, however, is that learning the motion flow instead allows the model to focus on the cause
of the blur, irrespective of the image content. This is a
much easier learning task, but it also avoids the iterative
process through which latent image priors are typically applied. Our approach directly estimates the motion flow from
the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from
the estimated motion flow. Our FCN is the first universal
end-to-end mapping from the blurred image to the dense
motion flow. To train the FCN, we simulate motion flows
to generate synthetic blurred-image-motion-flow pairs thus
avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate
that the proposed method outperforms the state-of-the-art