DANet: A Domain Alignment Network for Low-Light Image Enhancement

被引:0
|
作者
Li, Qiao [1 ]
Jiang, Bin [1 ]
Bo, Xiaochen [2 ]
Yang, Chao [1 ]
Wu, Xu [3 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
[2] Acad Mil Med Sci, Beijing 100850, Peoples R China
[3] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
关键词
low-light image enhancement; multi-domain alignment; convolution-transformer module;
D O I
10.3390/electronics13152954
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We propose restoring low-light images suffering from severe degradation using a deep-learning approach. A significant domain gap exists between low-light and real images, which previous methods have failed to address with domain alignment. To tackle this, we introduce a domain alignment network leveraging dual encoders and a domain alignment loss. Specifically, we train two dual encoders to transform low-light and real images into two latent spaces and align these spaces using a domain alignment loss. Additionally, we design a Convolution-Transformer module (CTM) during the encoding process to comprehensively extract both local and global features. Experimental results on four benchmark datasets demonstrate that our proposed A Domain Alignment Network(DANet) method outperforms state-of-the-art methods.
引用
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页数:15
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