  Most of the current face hallucination methods, whether they are shallow
learning-based or deep learning-based, all try to learn a relationship model
between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a
training set. They mainly focus on modeling image prior through either
model-based optimization or discriminative inference learning. However, when
the input LR face is tiny, the learned prior knowledge is no longer effective
and their performance will drop sharply. To solve this problem, in this paper
we propose a general face hallucination method that can integrate model-based
optimization and discriminative inference. In particular, to exploit the model
based prior, the Deep Convolutional Neural Networks (CNN) denoiser prior is
plugged into the super-resolution optimization model with the aid of
image-adaptive Laplacian regularization. Additionally, we further develop a
high-frequency details compensation method by dividing the face image to facial
components and performing face hallucination in a multi-layer neighbor
embedding manner. Experiments demonstrate that the proposed method can achieve
promising super-resolution results for tiny input LR faces.
