In this paper we address the problem of hallucinating high-resolution facial
images from unaligned low-resolution inputs at high magnification factors. We
approach the problem with convolutional neural networks (CNNs) and propose a
novel (deep) face hallucination model that incorporates identity priors into
the learning procedure. The model consists of two main parts: i) a cascaded
super-resolution network that upscales the low-resolution images, and ii) an
ensemble of face recognition models that act as identity priors for the
super-resolution network during training. Different from competing
super-resolution approaches that typically rely on a single model for upscaling
(even with large magnification factors), our network uses a cascade of multiple
SR models that progressively upscale the low-resolution images using steps of
$2\times$. This characteristic allows us to apply supervision signals (target
appearances) at different resolutions and incorporate identity constraints at
multiple-scales. Our model is able to upscale (very) low-resolution images
captured in unconstrained conditions and produce visually convincing results.
We rigorously evaluate the proposed model on a large datasets of facial images
and report superior performance compared to the state-of-the-art.

本文提出了一种基于卷积神经网络和脸部识别模型的（深度）脸部成像模型，通过使用级联超分辨率网络并将人脸识别模型作为学习过程中的先验来实现从低分辨率输入到高分辨率输出的脸部图像重建，实验结果表明该方法优于现有方法。