  Image deblurring aims to restore the latent sharp images from the
corresponding blurred ones. In this paper, we present an unsupervised method
for domain-specific single-image deblurring based on disentangled
representations. The disentanglement is achieved by splitting the content and
blur features in a blurred image using content encoders and blur encoders. We
enforce a KL divergence loss to regularize the distribution range of extracted
blur attributes such that little content information is contained. Meanwhile,
to handle the unpaired training data, a blurring branch and the
cycle-consistency loss are added to guarantee that the content structures of
the deblurred results match the original images. We also add an adversarial
loss on deblurred results to generate visually realistic images and a
perceptual loss to further mitigate the artifacts. We perform extensive
experiments on the tasks of face and text deblurring using both synthetic
datasets and real images, and achieve improved results compared to recent
state-of-the-art deblurring methods.
