Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

Ye Ji; Yaping Dai; Junjie Ma; Kaixin Zhao; Yanyan Cheng

doi:10.1109/CCDC49329.2020.9164664

Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

Ye Ji, Yaping Dai, Junjie Ma, Kaixin Zhao, Yanyan Cheng

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The research of image deblurring plays an important role in the digital image processing. In order to reduce image blurring problems, a content constraint loss (CCL) function in the generative adversarial network (GAN) is proposed. The SSIM loss and the perceptual loss constitute the CCL function, which makes the trained generative model stable. The CCL function as the content constraint loss component and the adversarial loss component constitute the total loss. The total loss is optimized by the iterative training to further improve the stability of the network model, and the image blurring will be reduced. In the test experiment of the open source image dataset MNIST, CIFAR10/100 and CELEBA, the CCL function is used as the content constraint loss component of the generative adversarial network, the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.

Original language	English
Title of host publication	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1985-1990
Number of pages	6
ISBN (Electronic)	9781728158549
DOIs	https://doi.org/10.1109/CCDC49329.2020.9164664
Publication status	Published - Aug 2020
Event	32nd Chinese Control and Decision Conference, CCDC 2020 - Hefei, China Duration: 22 Aug 2020 → 24 Aug 2020

Publication series

Name	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

Conference

Conference	32nd Chinese Control and Decision Conference, CCDC 2020
Country/Territory	China
City	Hefei
Period	22/08/20 → 24/08/20

Keywords

Adversarial loss
Content constraint loss
Generative adversarial network
Image deblurring

Access to Document

10.1109/CCDC49329.2020.9164664

Cite this

Ji, Y., Dai, Y., Ma, J., Zhao, K., & Cheng, Y. (2020). Generative Adversarial Network for Image Deblurring Using Content Constraint Loss. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020 (pp. 1985-1990). Article 9164664 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC49329.2020.9164664

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title = "Generative Adversarial Network for Image Deblurring Using Content Constraint Loss",

abstract = "The research of image deblurring plays an important role in the digital image processing. In order to reduce image blurring problems, a content constraint loss (CCL) function in the generative adversarial network (GAN) is proposed. The SSIM loss and the perceptual loss constitute the CCL function, which makes the trained generative model stable. The CCL function as the content constraint loss component and the adversarial loss component constitute the total loss. The total loss is optimized by the iterative training to further improve the stability of the network model, and the image blurring will be reduced. In the test experiment of the open source image dataset MNIST, CIFAR10/100 and CELEBA, the CCL function is used as the content constraint loss component of the generative adversarial network, the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.",

keywords = "Adversarial loss, Content constraint loss, Generative adversarial network, Image deblurring",

author = "Ye Ji and Yaping Dai and Junjie Ma and Kaixin Zhao and Yanyan Cheng",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 32nd Chinese Control and Decision Conference, CCDC 2020 ; Conference date: 22-08-2020 Through 24-08-2020",

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Ji, Y, Dai, Y, Ma, J, Zhao, K & Cheng, Y 2020, Generative Adversarial Network for Image Deblurring Using Content Constraint Loss. in Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020., 9164664, Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1985-1990, 32nd Chinese Control and Decision Conference, CCDC 2020, Hefei, China, 22/08/20. https://doi.org/10.1109/CCDC49329.2020.9164664

Generative Adversarial Network for Image Deblurring Using Content Constraint Loss. / Ji, Ye; Dai, Yaping; Ma, Junjie et al.
Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1985-1990 9164664 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

AU - Ji, Ye

AU - Dai, Yaping

AU - Ma, Junjie

AU - Zhao, Kaixin

AU - Cheng, Yanyan

PY - 2020/8

Y1 - 2020/8

N2 - The research of image deblurring plays an important role in the digital image processing. In order to reduce image blurring problems, a content constraint loss (CCL) function in the generative adversarial network (GAN) is proposed. The SSIM loss and the perceptual loss constitute the CCL function, which makes the trained generative model stable. The CCL function as the content constraint loss component and the adversarial loss component constitute the total loss. The total loss is optimized by the iterative training to further improve the stability of the network model, and the image blurring will be reduced. In the test experiment of the open source image dataset MNIST, CIFAR10/100 and CELEBA, the CCL function is used as the content constraint loss component of the generative adversarial network, the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.

AB - The research of image deblurring plays an important role in the digital image processing. In order to reduce image blurring problems, a content constraint loss (CCL) function in the generative adversarial network (GAN) is proposed. The SSIM loss and the perceptual loss constitute the CCL function, which makes the trained generative model stable. The CCL function as the content constraint loss component and the adversarial loss component constitute the total loss. The total loss is optimized by the iterative training to further improve the stability of the network model, and the image blurring will be reduced. In the test experiment of the open source image dataset MNIST, CIFAR10/100 and CELEBA, the CCL function is used as the content constraint loss component of the generative adversarial network, the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.

KW - Adversarial loss

KW - Content constraint loss

KW - Generative adversarial network

KW - Image deblurring

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T3 - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

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BT - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

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Ji Y, Dai Y, Ma J, Zhao K, Cheng Y. Generative Adversarial Network for Image Deblurring Using Content Constraint Loss. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1985-1990. 9164664. (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). doi: 10.1109/CCDC49329.2020.9164664

Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

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