Multi-Exposure Image Fusion Through Feature Decomposition

Jong Han Kim; Kang Kyu Lee; Jong Ok Kim

doi:10.1109/ICCE-Asia53811.2021.9642010

Multi-Exposure Image Fusion Through Feature Decomposition

Jong Han Kim, Kang Kyu Lee, Jong Ok Kim

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

Abstract

Multi-exposure image fusion is an effective method for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) image. The previous methods suffer from poor detail and color restoration performance and visual artifact, such as halo. In this paper, to overcome these problems, we propose a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition and RGB channel fusion. A feature of LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the respective common and residual domain. It is found through diverse experiments that the proposed network could improve the MEF performance in aspects of color restoration and visual artifact.

Original language	English
Title of host publication	2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665408578
DOIs	https://doi.org/10.1109/ICCE-Asia53811.2021.9642010
Publication status	Published - 2021
Event	2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 - Gangwon, Korea, Republic of Duration: 2021 Nov 1 → 2021 Nov 3

Publication series

Name	2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021

Conference

Conference	2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021
Country/Territory	Korea, Republic of
City	Gangwon
Period	21/11/1 → 21/11/3

Bibliographical note

Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01421) supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation).

Publisher Copyright:
© 2021 IEEE.

Keywords

Color restoration
Detail restoration
Feature decomposition
Halo artifact reduction
Multi-exposure image fusion

ASJC Scopus subject areas

Instrumentation
Computer Networks and Communications
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICCE-Asia53811.2021.9642010

Cite this

Kim, J. H., Lee, K. K., & Kim, J. O. (2021). Multi-Exposure Image Fusion Through Feature Decomposition. In 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 (2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Asia53811.2021.9642010

Multi-Exposure Image Fusion Through Feature Decomposition. / Kim, Jong Han; Lee, Kang Kyu; Kim, Jong Ok.
2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021).

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

Kim, JH, Lee, KK & Kim, JO 2021, Multi-Exposure Image Fusion Through Feature Decomposition. in 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021. 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021, Gangwon, Korea, Republic of, 21/11/1. https://doi.org/10.1109/ICCE-Asia53811.2021.9642010

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title = "Multi-Exposure Image Fusion Through Feature Decomposition",

abstract = "Multi-exposure image fusion is an effective method for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) image. The previous methods suffer from poor detail and color restoration performance and visual artifact, such as halo. In this paper, to overcome these problems, we propose a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition and RGB channel fusion. A feature of LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the respective common and residual domain. It is found through diverse experiments that the proposed network could improve the MEF performance in aspects of color restoration and visual artifact.",

keywords = "Color restoration, Detail restoration, Feature decomposition, Halo artifact reduction, Multi-exposure image fusion",

author = "Kim, {Jong Han} and Lee, {Kang Kyu} and Kim, {Jong Ok}",

note = "Funding Information: This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01421) supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 ; Conference date: 01-11-2021 Through 03-11-2021",

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doi = "10.1109/ICCE-Asia53811.2021.9642010",

language = "English",

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N1 - Funding Information: This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01421) supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation). Publisher Copyright: © 2021 IEEE.

PY - 2021

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N2 - Multi-exposure image fusion is an effective method for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) image. The previous methods suffer from poor detail and color restoration performance and visual artifact, such as halo. In this paper, to overcome these problems, we propose a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition and RGB channel fusion. A feature of LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the respective common and residual domain. It is found through diverse experiments that the proposed network could improve the MEF performance in aspects of color restoration and visual artifact.

AB - Multi-exposure image fusion is an effective method for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) image. The previous methods suffer from poor detail and color restoration performance and visual artifact, such as halo. In this paper, to overcome these problems, we propose a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition and RGB channel fusion. A feature of LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the respective common and residual domain. It is found through diverse experiments that the proposed network could improve the MEF performance in aspects of color restoration and visual artifact.

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T3 - 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021

BT - 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Multi-Exposure Image Fusion Through Feature Decomposition

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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