WGAN based edge preserved de-blurring using perceptual style similarity

Minsoo Hong; Yoonsik Choe

doi:10.1109/IS3C.2018.00023

WGAN based edge preserved de-blurring using perceptual style similarity

Minsoo Hong, Yoonsik Choe

Department of Electrical and Electronic Engineering

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

1 Citation (Scopus)

Abstract

Generative Adversarial Network (GAN) is an effective generative model and can be used for de-blurring. In this paper, we propose an edge preserved de-blurring method using Wasserstein generative adversarial network with gradient penalty (WGAN-GP), which is based on conditional GAN. Also, since detailed-edge is the most important factor in de-blurred image, in order to preserve detailed-edge and capture its perceptual similarity, the style loss function is added to represent the perceptual information of the edge. Consequently, the proposed method improves the similarity between sharp images and blurred images by minimizing Wasserstein distance, and well captures the perceptual similarity using style loss function, considering the correlation of features in Convolutional Neural Network. Experiments depict that the proposed method achieves 0.98 in SSIM that is higher performance, compared to other conventional methods such as filter based methods and content based method.

Original language	English
Title of host publication	Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	58-61
Number of pages	4
ISBN (Electronic)	9781538670361
DOIs	https://doi.org/10.1109/IS3C.2018.00023
Publication status	Published - 2018 Jul 2
Event	4th International Symposium on Computer, Consumer and Control, IS3C 2018 - Taichung, Taiwan, Province of China Duration: 2018 Dec 6 → 2018 Dec 8

Publication series

Name	Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018

Conference

Conference	4th International Symposium on Computer, Consumer and Control, IS3C 2018
Country/Territory	Taiwan, Province of China
City	Taichung
Period	18/12/6 → 18/12/8

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Control and Systems Engineering
Energy Engineering and Power Technology
Computer Science Applications
Control and Optimization
Signal Processing

Access to Document

10.1109/IS3C.2018.00023

Cite this

Hong, M., & Choe, Y. (2018). WGAN based edge preserved de-blurring using perceptual style similarity. In Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018 (pp. 58-61). Article 8644758 (Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IS3C.2018.00023

@inproceedings{5408684aa2eb46a18d8061f9f1766b9c,

title = "WGAN based edge preserved de-blurring using perceptual style similarity",

abstract = "Generative Adversarial Network (GAN) is an effective generative model and can be used for de-blurring. In this paper, we propose an edge preserved de-blurring method using Wasserstein generative adversarial network with gradient penalty (WGAN-GP), which is based on conditional GAN. Also, since detailed-edge is the most important factor in de-blurred image, in order to preserve detailed-edge and capture its perceptual similarity, the style loss function is added to represent the perceptual information of the edge. Consequently, the proposed method improves the similarity between sharp images and blurred images by minimizing Wasserstein distance, and well captures the perceptual similarity using style loss function, considering the correlation of features in Convolutional Neural Network. Experiments depict that the proposed method achieves 0.98 in SSIM that is higher performance, compared to other conventional methods such as filter based methods and content based method.",

author = "Minsoo Hong and Yoonsik Choe",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th International Symposium on Computer, Consumer and Control, IS3C 2018 ; Conference date: 06-12-2018 Through 08-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/IS3C.2018.00023",

language = "English",

series = "Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "58--61",

booktitle = "Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018",

address = "United States",

}

Hong, M & Choe, Y 2018, WGAN based edge preserved de-blurring using perceptual style similarity. in Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018., 8644758, Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018, Institute of Electrical and Electronics Engineers Inc., pp. 58-61, 4th International Symposium on Computer, Consumer and Control, IS3C 2018, Taichung, Taiwan, Province of China, 18/12/6. https://doi.org/10.1109/IS3C.2018.00023

WGAN based edge preserved de-blurring using perceptual style similarity. / Hong, Minsoo; Choe, Yoonsik.
Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 58-61 8644758 (Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018).

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

TY - GEN

T1 - WGAN based edge preserved de-blurring using perceptual style similarity

AU - Hong, Minsoo

AU - Choe, Yoonsik

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Generative Adversarial Network (GAN) is an effective generative model and can be used for de-blurring. In this paper, we propose an edge preserved de-blurring method using Wasserstein generative adversarial network with gradient penalty (WGAN-GP), which is based on conditional GAN. Also, since detailed-edge is the most important factor in de-blurred image, in order to preserve detailed-edge and capture its perceptual similarity, the style loss function is added to represent the perceptual information of the edge. Consequently, the proposed method improves the similarity between sharp images and blurred images by minimizing Wasserstein distance, and well captures the perceptual similarity using style loss function, considering the correlation of features in Convolutional Neural Network. Experiments depict that the proposed method achieves 0.98 in SSIM that is higher performance, compared to other conventional methods such as filter based methods and content based method.

AB - Generative Adversarial Network (GAN) is an effective generative model and can be used for de-blurring. In this paper, we propose an edge preserved de-blurring method using Wasserstein generative adversarial network with gradient penalty (WGAN-GP), which is based on conditional GAN. Also, since detailed-edge is the most important factor in de-blurred image, in order to preserve detailed-edge and capture its perceptual similarity, the style loss function is added to represent the perceptual information of the edge. Consequently, the proposed method improves the similarity between sharp images and blurred images by minimizing Wasserstein distance, and well captures the perceptual similarity using style loss function, considering the correlation of features in Convolutional Neural Network. Experiments depict that the proposed method achieves 0.98 in SSIM that is higher performance, compared to other conventional methods such as filter based methods and content based method.

UR - http://www.scopus.com/inward/record.url?scp=85063211506&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063211506&partnerID=8YFLogxK

U2 - 10.1109/IS3C.2018.00023

DO - 10.1109/IS3C.2018.00023

M3 - Conference contribution

AN - SCOPUS:85063211506

T3 - Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018

SP - 58

EP - 61

BT - Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Symposium on Computer, Consumer and Control, IS3C 2018

Y2 - 6 December 2018 through 8 December 2018

ER -

Hong M, Choe Y. WGAN based edge preserved de-blurring using perceptual style similarity. In Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 58-61. 8644758. (Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018). doi: 10.1109/IS3C.2018.00023

WGAN based edge preserved de-blurring using perceptual style similarity

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this