Face super-resolution technology based on generative adversarial networks

ZHANG Tianlu; YANG Jie; HE Wenyu; JIA Limin

doi:10.13873/j.1000-9787(2020)02-0040-04

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Face super-resolution technology based on generative adversarial networks

更新时间：2025-09-11

- Face super-resolution technology based on generative adversarial networks
- Issue 2, Pages: 40-43(2020)
- 作者机构：
  
  江西理工大学电气工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2020)02-0040-04
  CLC： TP18;TP391.41
- Published：2020
- 稿件说明：
移动端阅览
ZHANG Tianlu, YANG Jie, HE Wenyu, et al. Face super-resolution technology based on generative adversarial networks[J]. 2020, (2): 40-43.
DOI：

ZHANG Tianlu, YANG Jie, HE Wenyu, et al. Face super-resolution technology based on generative adversarial networks[J]. 2020, (2): 40-43. DOI： 10.13873/j.1000-9787(2020)02-0040-04.

摘要

针对传统方法只能提高人脸图像中局部区域的清晰度

并存在整体人脸图像钝化模糊等问题

提出了一种基于生成对抗网络的人脸超分辨率方法。通过PSNR值、SSIM值以及MOS测试的综合考量

得出基于该方法的人脸超分辨率技术相比于其他传统图像处理方法或SRCNN

DRCN

ESPCN等深度学习方法具有很大的优势

其生成的超分辨率人脸在提高了放大倍数的同时

还获得了更好的人脸高频细节。

Abstract

Aiming at the problem that traditional methods can only increase the resolution of local regions in face images

and the whole face image passivation blur is also present

a face super-resolution method based on generative adversarial networks is proposed. Through the PSNR value

SSIM value and MOS test comprehensive considerations

it is concluded that the face super-resolution technology based on this method has great advantages compared with other traditional image processing methods or deep learning methods such as SRCNN

DRCN

and ESPCN

etc

the super-resolution face generated by this method not only improves the magnification

but also obtains better high-frequency details of face.

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