  Existing face hallucination methods based on convolutional neural networks
(CNN) have achieved impressive performance on low-resolution (LR) faces in a
normal illumination condition. However, their performance degrades dramatically
when LR faces are captured in low or non-uniform illumination conditions. This
paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to
recover authentic high-resolution (HR) face images while compensating for low
and non-uniform illumination. To this end, we develop two key components in our
CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our
internal CPnet exploits facial information residing in the input image to
enhance facial details; while our external CPnet leverages an external HR face
for illumination compensation. A new illumination compensation loss is thus
developed to capture illumination from the external guided face image
effectively. Furthermore, our method offsets illumination and upsamples facial
details alternately in a coarse-to-fine fashion, thus alleviating the
correspondence ambiguity between LR inputs and external HR inputs. Extensive
experiments demonstrate that our method manifests authentic HR face images in a
uniform illumination condition and outperforms state-of-the-art methods
qualitatively and quantitatively.
