CNN Based Multi-view Image Quality Enhancement

Gyu Lee Jeon; Hee Jae Kim; Eun Yeo; Je Won Kang

doi:10.1109/ICUFN55119.2022.9829639

CNN Based Multi-view Image Quality Enhancement

Gyu Lee Jeon, Hee Jae Kim, Eun Yeo, Je Won Kang

Electronic and Electrical Engineering

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

2 Scopus citations

Abstract

We propose a CNN-based multi-view image quality enhancement (MVIQE) to improve the quality of a target image using adjacent multi-view images. Differing from the conventional single frame quality enhancement, our approach aims to improve the quality by transferring a higher quality of other input views in multi-view images as references. Our network contains an optical flow estimation module, warping layers, and image synthesis module to enhance the quality of a target image with quantization noise. Experimental results show that our method outperforms previous studies on image quality enhancement in terms of peak signal-to-noise ratio performance.

Original language	English
Title of host publication	ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	372-375
Number of pages	4
ISBN (Electronic)	9781665485500
DOIs	https://doi.org/10.1109/ICUFN55119.2022.9829639
State	Published - 2022
Event	13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 - Virtual, Barcelona, Spain Duration: 5 Jul 2022 → 8 Jul 2022

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2022-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	13th International Conference on Ubiquitous and Future Networks, ICUFN 2022
Country/Territory	Spain
City	Virtual, Barcelona
Period	5/07/22 → 8/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Convolutional Neural Networks(CNN)
Image quality enhancement
Keywords
multi-view images

Access to Document

10.1109/ICUFN55119.2022.9829639

Cite this

@inproceedings{95cbc122bbbe4236a5ff1611a66a1150,

title = "CNN Based Multi-view Image Quality Enhancement",

abstract = "We propose a CNN-based multi-view image quality enhancement (MVIQE) to improve the quality of a target image using adjacent multi-view images. Differing from the conventional single frame quality enhancement, our approach aims to improve the quality by transferring a higher quality of other input views in multi-view images as references. Our network contains an optical flow estimation module, warping layers, and image synthesis module to enhance the quality of a target image with quantization noise. Experimental results show that our method outperforms previous studies on image quality enhancement in terms of peak signal-to-noise ratio performance.",

keywords = "Convolutional Neural Networks(CNN), Image quality enhancement, Keywords, multi-view images",

author = "Jeon, {Gyu Lee} and Kim, {Hee Jae} and Eun Yeo and Kang, {Je Won}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 ; Conference date: 05-07-2022 Through 08-07-2022",

year = "2022",

doi = "10.1109/ICUFN55119.2022.9829639",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

publisher = "IEEE Computer Society",

pages = "372--375",

booktitle = "ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks",

}

Jeon, GL, Kim, HJ, Yeo, E & Kang, JW 2022, CNN Based Multi-view Image Quality Enhancement. in ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2022-July, IEEE Computer Society, pp. 372-375, 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022, Virtual, Barcelona, Spain, 5/07/22. https://doi.org/10.1109/ICUFN55119.2022.9829639

CNN Based Multi-view Image Quality Enhancement. / Jeon, Gyu Lee; Kim, Hee Jae; Yeo, Eun et al.
ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2022. p. 372-375 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2022-July).

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

TY - GEN

T1 - CNN Based Multi-view Image Quality Enhancement

AU - Jeon, Gyu Lee

AU - Kim, Hee Jae

AU - Yeo, Eun

AU - Kang, Je Won

PY - 2022

Y1 - 2022

N2 - We propose a CNN-based multi-view image quality enhancement (MVIQE) to improve the quality of a target image using adjacent multi-view images. Differing from the conventional single frame quality enhancement, our approach aims to improve the quality by transferring a higher quality of other input views in multi-view images as references. Our network contains an optical flow estimation module, warping layers, and image synthesis module to enhance the quality of a target image with quantization noise. Experimental results show that our method outperforms previous studies on image quality enhancement in terms of peak signal-to-noise ratio performance.

AB - We propose a CNN-based multi-view image quality enhancement (MVIQE) to improve the quality of a target image using adjacent multi-view images. Differing from the conventional single frame quality enhancement, our approach aims to improve the quality by transferring a higher quality of other input views in multi-view images as references. Our network contains an optical flow estimation module, warping layers, and image synthesis module to enhance the quality of a target image with quantization noise. Experimental results show that our method outperforms previous studies on image quality enhancement in terms of peak signal-to-noise ratio performance.

KW - Convolutional Neural Networks(CNN)

KW - Image quality enhancement

KW - Keywords

KW - multi-view images

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U2 - 10.1109/ICUFN55119.2022.9829639

DO - 10.1109/ICUFN55119.2022.9829639

M3 - Conference contribution

AN - SCOPUS:85135237816

T3 - International Conference on Ubiquitous and Future Networks, ICUFN

SP - 372

EP - 375

BT - ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks

PB - IEEE Computer Society

T2 - 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022

Y2 - 5 July 2022 through 8 July 2022

ER -

CNN Based Multi-view Image Quality Enhancement

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this