A Unified Framework to Super-Resolve Face Images of Varied Low
  Resolutions
Qiuyu Peng Zifei Jiang Yan Huang Jingliang Peng
The existing face image super-resolution (FSR) algorithms usually train a
specific model for a specific low input resolution for optimal results. By
contrast, we explore in this work a unified framework that is trained once and
then used to super-resolve input face images of varied low resolutions. For
that purpose, we propose a novel neural network architecture that is composed
of three anchor auto-encoders, one feature weight regressor and a final image
decoder. The three anchor auto-encoders are meant for optimal FSR for three
pre-defined low input resolutions, or named anchor resolutions, respectively.
An input face image of an arbitrary low resolution is firstly up-scaled to the
target resolution by bi-cubic interpolation and then fed to the three
auto-encoders in parallel. The three encoded anchor features are then fused
with weights determined by the feature weight regressor. At last, the fused
feature is sent to the final image decoder to derive the super-resolution
result. As shown by experiments, the proposed algorithm achieves robust and
state-of-the-art performance over a wide range of low input resolutions by a
single framework. Code and models will be made available after the publication
of this work.