  As a domain-specific super-resolution problem, facial image hallucination has
enjoyed a series of breakthroughs thanks to the advances of deep convolutional
neural networks. However, the direct migration of existing methods to video is
still difficult to achieve good performance due to its lack of alignment and
consistency modelling in temporal domain. Taking advantage of high inter-frame
dependency in videos, we propose a self-enhanced convolutional network for
facial video hallucination. It is implemented by making full usage of preceding
super-resolved frames and a temporal window of adjacent low-resolution frames.
Specifically, the algorithm first obtains the initial high-resolution inference
of each frame by taking into consideration a sequence of consecutive
low-resolution inputs through temporal consistency modelling. It further
recurrently exploits the reconstructed results and intermediate features of a
sequence of preceding frames to improve the initial super-resolution of the
current frame by modelling the coherence of structural facial features across
frames. Quantitative and qualitative evaluations demonstrate the superiority of
the proposed algorithm against state-of-the-art methods. Moreover, our
algorithm also achieves excellent performance in the task of general video
super-resolution in a single-shot setting.
