  Facial image super-resolution (SR) is an important preprocessing for facial
image analysis, face recognition, and image-based 3D face reconstruction.
Recent convolutional neural network (CNN) based method has shown excellent
performance by learning mapping relation using pairs of low-resolution (LR) and
high-resolution (HR) facial images. However, since the HR facial image
reconstruction using CNN is conventionally aimed to increase the PSNR and SSIM
metrics, the reconstructed HR image might not be realistic even with high
scores. An adversarial framework is proposed in this study to reconstruct the
HR facial image by simultaneously generating an HR image with and without blur.
First, the spatial resolution of the LR facial image is increased by eight
times using a five-layer CNN. Then, the encoder extracts the features of the
up-scaled image. These features are finally sent to two branches (decoders) to
generate an HR facial image with and without blur. In addition, local and
global discriminators are combined to focus on the reconstruction of HR facial
structures. Experiment results show that the proposed algorithm generates a
realistic HR facial image. Furthermore, the proposed method can generate a
variety of different facial images.
