  Blind deblurring consists a long studied task, however the outcomes of
generic methods are not effective in real world blurred images. Domain-specific
methods for deblurring targeted object categories, e.g. text or faces,
frequently outperform their generic counterparts, hence they are attracting an
increasing amount of attention. In this work, we develop such a domain-specific
method to tackle deblurring of human faces, henceforth referred to as face
deblurring. Studying faces is of tremendous significance in computer vision,
however face deblurring has yet to demonstrate some convincing results. This
can be partly attributed to the combination of i) poor texture and ii) highly
structure shape that yield the contour/gradient priors (that are typically
used) sub-optimal. In our work instead of making assumptions over the prior, we
adopt a learning approach by inserting weak supervision that exploits the
well-documented structure of the face. Namely, we utilise a deep network to
perform the deblurring and employ a face alignment technique to pre-process
each face. We additionally surpass the requirement of the deep network for
thousands training samples, by introducing an efficient framework that allows
the generation of a large dataset. We utilised this framework to create 2MF2, a
dataset of over two million frames. We conducted experiments with real world
blurred facial images and report that our method returns a result close to the
sharp natural latent image.
