  In this paper, we propose an effective and efficient face deblurring
algorithm by exploiting semantic cues via deep convolutional neural networks.
As the human faces are highly structured and share unified facial components
(e.g., eyes and mouths), such semantic information provides a strong prior for
restoration. We incorporate face semantic labels as input priors and propose an
adaptive structural loss to regularize facial local structures within an
end-to-end deep convolutional neural network. Specifically, we first use a
coarse deblurring network to reduce the motion blur on the input face image. We
then adopt a parsing network to extract the semantic features from the coarse
deblurred image. Finally, the fine deblurring network utilizes the semantic
information to restore a clear face image. We train the network with perceptual
and adversarial losses to generate photo-realistic results. The proposed method
restores sharp images with more accurate facial features and details.
Quantitative and qualitative evaluations demonstrate that the proposed face
deblurring algorithm performs favorably against the state-of-the-art methods in
terms of restoration quality, face recognition and execution speed.
