  Conventional face super-resolution methods usually assume testing
low-resolution (LR) images lie in the same domain as the training ones. Due to
different lighting conditions and imaging hardware, domain gaps between
training and testing images inevitably occur in many real-world scenarios.
Neglecting those domain gaps would lead to inferior face super-resolution (FSR)
performance. However, how to transfer a trained FSR model to a target domain
efficiently and effectively has not been investigated. To tackle this problem,
we develop a Domain-Aware Pyramid-based Face Super-Resolution network, named
DAP-FSR network. Our DAP-FSR is the first attempt to super-resolve LR faces
from a target domain by exploiting only a pair of high-resolution (HR) and LR
exemplar in the target domain. To be specific, our DAP-FSR firstly employs its
encoder to extract the multi-scale latent representations of the input LR face.
Considering only one target domain example is available, we propose to augment
the target domain data by mixing the latent representations of the target
domain face and source domain ones, and then feed the mixed representations to
the decoder of our DAP-FSR. The decoder will generate new face images
resembling the target domain image style. The generated HR faces in turn are
used to optimize our decoder to reduce the domain gap. By iteratively updating
the latent representations and our decoder, our DAP-FSR will be adapted to the
target domain, thus achieving authentic and high-quality upsampled HR faces.
Extensive experiments on three newly constructed benchmarks validate the
effectiveness and superior performance of our DAP-FSR compared to the
state-of-the-art.
