TY - GEN
T1 - Multi-Exposure Image Fusion Through Feature Decomposition
AU - Kim, Jong Han
AU - Lee, Kang Kyu
AU - Kim, Jong Ok
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
Y1 - 2021
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.
KW - Color restoration
KW - Detail restoration
KW - Feature decomposition
KW - Halo artifact reduction
KW - Multi-exposure image fusion
UR - http://www.scopus.com/inward/record.url?scp=85123761383&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Asia53811.2021.9642010
DO - 10.1109/ICCE-Asia53811.2021.9642010
M3 - Conference contribution
AN - SCOPUS:85123761383
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.
T2 - 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021
Y2 - 1 November 2021 through 3 November 2021
ER -