FDD-MEF: Feature-Decomposition-Based Deep Multi-Exposure Fusion
- Authors
- Kim, Jong-Han; Ryu, Je-Ho; Kim, Jong-Ok
- Issue Date
- 2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Deep multi-exposure image fusion; Dynamic range; Feature extraction; Image color analysis; Image fusion; Image reconstruction; Image restoration; Visualization; color restoration; detail restoration; feature decomposition; halo artifact reduction
- Citation
- IEEE ACCESS, v.9, pp.164551 - 164561
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 9
- Start Page
- 164551
- End Page
- 164561
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/138710
- DOI
- 10.1109/ACCESS.2021.3134316
- ISSN
- 2169-3536
- Abstract
- Multi-exposure image fusion is an effective algorithm for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) images. In this study, a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition is proposed. The conventional MEF methods are weak for restoring detail and color, and they suffer from visual artifacts. To overcome these challenges, a feature of each LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the residual domain. It was found through diverse experiments that the proposed network could improve the MEF performance in three aspects; detail restoration in bright and dark regions, reduction of halo artifacts, and natural color restoration. In addition, an attempt was made to find the underlying principles of feature-decomposition-based MEF by visualizing the features through RGB channels.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.