Learning to Hallucinate Face Images via Component Generation and Enhancement

Face hallucination aims to generate high resolution face images from low resolution inputs. It has a series of applications ranging from video surveillance to image editing. Under the data-driven framework which consists of low and high resolution image pairs, the main difficulty is to establish the correspondence between the low resolution input and high resolution training data. In comparison, existing GAN based CNN networks contain the limitation that they do not favor high numerial scores. These networks are not precisely set to measure the pixel wise difference. It results in the incorrect alignment between the upsampled facial structure output and that of ground truth even though the overall perception seems more appropriate.

@inproceedings{song-ijcai17-faceSR,
  author    = {Song, Yibing and Zhang, Jiawei and He, Shengfeng and Bao, Linchao and Yang, Qingxiong},
  title     = {Learning to Hallucinate Face Images via Component Generation and Enhancement},
  booktitle = {International Joint Conference on Artificial Intelligence},
  pages     = {4537--4543},
  year      = {2017},  
}