  We propose a very fast and effective one-step restoring method for blurry
face images. In the last decades, many blind deblurring algorithms have been
proposed to restore latent sharp images. However, these algorithms run slowly
because of involving two steps: kernel estimation and following non-blind
deconvolution or latent image estimation. Also they cannot handle face images
in small size. Our proposed method restores sharp face images directly in one
step using Convolutional Neural Network. Unlike previous deep learning involved
methods that can only handle a single blur kernel at one time, our network is
trained on totally random and numerous training sample pairs to deal with the
variances due to different blur kernels in practice. A smoothness
regularization as well as a facial regularization are added to keep facial
identity information which is the key to face image applications. Comprehensive
experiments demonstrate that our proposed method can handle various blur kenels
and achieve state-of-the-art results for small size blurry face images
restoration. Moreover, the proposed method shows significant improvement in
face recognition accuracy along with increasing running speed by more than 100
times.
