Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Survey on GAN-based face hallucination with its model development
AU - Liu, H.
AU - Zheng, X.
AU - Han, Jungong
AU - Chu, Y.
AU - Tao, T.
PY - 2019/12/12
Y1 - 2019/12/12
N2 - Face hallucination aims to produce a high-resolution face image from an input low-resolution face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure of the face image is complex and sensitive, obtaining a super-resolved face image is more difficult than generic image super-resolution. Recently, with great success in the high-level face recognition task, deep learning methods, especially generative adversarial networks (GANs), have also been applied to the low-level vision task – face hallucination. This work is to provide a model evolvement survey on GAN-based face hallucination. The principles of image resolution degradation and GAN-based learning are presented firstly. Then, a comprehensive review of the state-of-art GAN-based face hallucination methods is provided. Finally, the comparisons of these GAN-based face hallucination methods and the discussions of the related issues for future research direction are also provided.
AB - Face hallucination aims to produce a high-resolution face image from an input low-resolution face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure of the face image is complex and sensitive, obtaining a super-resolved face image is more difficult than generic image super-resolution. Recently, with great success in the high-level face recognition task, deep learning methods, especially generative adversarial networks (GANs), have also been applied to the low-level vision task – face hallucination. This work is to provide a model evolvement survey on GAN-based face hallucination. The principles of image resolution degradation and GAN-based learning are presented firstly. Then, a comprehensive review of the state-of-art GAN-based face hallucination methods is provided. Finally, the comparisons of these GAN-based face hallucination methods and the discussions of the related issues for future research direction are also provided.
U2 - 10.1049/iet-ipr.2018.6545
DO - 10.1049/iet-ipr.2018.6545
M3 - Journal article
VL - 13
SP - 2662
EP - 2672
JO - IET Image Processing
JF - IET Image Processing
SN - 1751-9659
IS - 14
ER -