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Fourier-based two-stage low-light image enhancement network via mutual learning

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成果类型:
期刊论文
作者:
Zhang, Jianming;Feng, Zhijian;Jiang, Jia;Gui, Yan
通讯作者:
Zhang, JM
作者机构:
[Zhang, Jianming; Jiang, Jia; Feng, Zhijian] Changsha Univ Sci & Technol, Key Lab Safety Control Bridge Engn, Minist Educ, Changsha 410114, Peoples R China.
[Zhang, JM; Gui, Yan; Zhang, Jianming; Jiang, Jia; Feng, Zhijian] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410076, Peoples R China.
通讯机构:
[Zhang, JM ] C
Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410076, Peoples R China.
语种:
英文
关键词:
Fourier;Mutual learning;Low-light image enhancement;Light-weight
期刊:
Digital Signal Processing
ISSN:
1051-2004
年:
2025
卷:
160
页码:
105044
基金类别:
CRediT authorship contribution statement Jianming Zhang: Conceptualization, Formal analysis, acquisition, Methodology, Supervision, Writing – review & editing. Zhijian Feng: Formal analysis, Investigation, Methodology, Software, Writing – original draft. Jia Jiang: Data curation, Investigation, Visualization. Yan Gui: Resources, Validation.
机构署名:
本校为第一且通讯机构
院系归属:
计算机与通信工程学院
土木工程学院
摘要:
Images captured under improper exposure conditions lose their brightness information and texture details. Therefore, the enhancement of low-light images has received widespread attention. In recent years, most methods are based on deep convolutional neural networks to enhance low-light images in the spatial domain, which tends to introduce a huge number of parameters, thus limiting their practical applicability. In this paper, we propose a Fourier-based two-stage low-light image enhancement method via mutual learning (FT-LLIE), which sequentially enhance the amplitude and phase components. Spe...

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