FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To...

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Autores principales:	Tongxin Wei, Qingbao Li, Zhifeng Chen, Jinjin Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/7ebc1be710db4890a41fd2491088c870
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spelling	oai:doaj.org-article:7ebc1be710db4890a41fd2491088c8702021-11-08T02:37:28ZFRGAN: A Blind Face Restoration with Generative Adversarial Networks1563-514710.1155/2021/2384435https://doaj.org/article/7ebc1be710db4890a41fd2491088c8702021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2384435https://doaj.org/toc/1563-5147Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.Tongxin WeiQingbao LiZhifeng ChenJinjin LiuHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Tongxin Wei Qingbao Li Zhifeng Chen Jinjin Liu FRGAN: A Blind Face Restoration with Generative Adversarial Networks
description	Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.
format	article
author	Tongxin Wei Qingbao Li Zhifeng Chen Jinjin Liu
author_facet	Tongxin Wei Qingbao Li Zhifeng Chen Jinjin Liu
author_sort	Tongxin Wei
title	FRGAN: A Blind Face Restoration with Generative Adversarial Networks
title_short	FRGAN: A Blind Face Restoration with Generative Adversarial Networks
title_full	FRGAN: A Blind Face Restoration with Generative Adversarial Networks
title_fullStr	FRGAN: A Blind Face Restoration with Generative Adversarial Networks
title_full_unstemmed	FRGAN: A Blind Face Restoration with Generative Adversarial Networks
title_sort	frgan: a blind face restoration with generative adversarial networks
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/7ebc1be710db4890a41fd2491088c870
work_keys_str_mv	AT tongxinwei frganablindfacerestorationwithgenerativeadversarialnetworks AT qingbaoli frganablindfacerestorationwithgenerativeadversarialnetworks AT zhifengchen frganablindfacerestorationwithgenerativeadversarialnetworks AT jinjinliu frganablindfacerestorationwithgenerativeadversarialnetworks
_version_	1718443014538395648

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

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