High-quality face image super-resolution based on Generative Adversarial Networks

Xinru Zhong, Xiujie Qu, Chen Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

Face image super-resolution has received increasing attention. However, since the face has a lot of fine textures, it is very difficult to rebuild for large upscaling factors. We propose a new method for face image SR, using residul dense block(RDB) as the basic unit and the Inception architecture is combined in the low layers. We use the relativistic GAN and the improved perceptual loss defined by the features before activation.For the large scaling factors, our GAN is progressive both in architecture and training. The network proposed achieves excellent performance in the reconstruction of low-resolution face images, especially under large scaling factors such as 4x and 8x.

源语言英语
主期刊名Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019
编辑Bing Xu, Kefen Mou
出版商Institute of Electrical and Electronics Engineers Inc.
1178-1182
页数5
ISBN(电子版)9781728119076
DOI
出版状态已出版 - 12月 2019
活动4th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019 - Chengdu, 中国
期限: 20 12月 201922 12月 2019

出版系列

姓名Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019

会议

会议4th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019
国家/地区中国
Chengdu
时期20/12/1922/12/19

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