Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement
文献类型：期刊论文
| 作者 | Xu, Jingzhao1; Yuan, Mengke2,3 ; Yan, Dong-Ming2,3; Wu, Tieru1
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2023 |
| 卷号 | 25页码:6258-6271 |
| ISSN号 | 1520-9210 |
| 关键词 | Lighting Wavelet transforms Image enhancement Frequency modulation Wavelet coefficients Noise reduction Discrete wavelet transforms Attention mechanism illumination guidance low-light image enhancement wavelet transform |
| DOI | 10.1109/TMM.2022.3207330 |
| 通讯作者 | Yuan, Mengke(mengke.yuan@nlpr.ia.ac.cn) ; Wu, Tieru(wutr@jlu.edu.cn) |
| 英文摘要 | Deep convolutional neural networks have recently been applied to improve the quality of low-light images and have achieved promising results. However, most existing methods cannot suppress noise during the enhancement process effectively, resulting in unknown artifacts and color distortions. In addition, these methods do not fully utilize illumination information and perform poorly under extremely low-light condition. To alleviate these problems, we propose the illumination guided attentive wavelet network (IGAWN) for low-light image enhancement (LLIE). Considering that the wavelet transform can separate high-frequency noise and desired low-frequency content effectively, we enhance low-light images in the frequency domain. By integrating attention mechanisms with wavelet transform, we develop the attentive wavelet transform to capture more important wavelet features, which enables the desired content to be enhanced and the redundant noise to be suppressed. To improve the image enhancement performance under extremely low-light environment, we extract illumination information from the input images and exploit it as the guidance for image enhancement through the frequency feature transform(FFT) layer. The proposed FFT layer generates frequency-aware affine transformation from the estimated illumination information, which can adaptively modulate the image features of different frequencies. Extensive experiments on synthetic and real-world datasets demonstrate that our IGAWNperforms favorably against state-of-the-art LLIE methods. |
| WOS关键词 | HISTOGRAM EQUALIZATION ; QUALITY ASSESSMENT ; RETINEX |
| 资助项目 | National Key Research and Development Program of China[2020YFA0714101] ; National Nature Science Foundation of China[61872162] ; National Nature Science Foundation of China[62102414] ; National Nature Science Foundation of China[62172415] ; National Nature Science Foundation of China[52175493] ; Open Research Fund Program of State key Laboratory of Hydroscience and Engineering[sklhse-2022-D-04] ; Alibaba Group through Alibaba Innovative Research Program |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001098831500044 |
| 资助机构 | National Key Research and Development Program of China ; National Nature Science Foundation of China ; Open Research Fund Program of State key Laboratory of Hydroscience and Engineering ; Alibaba Group through Alibaba Innovative Research Program |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55165]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Yuan, Mengke; Wu, Tieru |
| 作者单位 | 1.Jilin Univ, Sch Math, Changchun 130012, Peoples R China 2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Jingzhao,Yuan, Mengke,Yan, Dong-Ming,et al. Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:6258-6271. |
| APA | Xu, Jingzhao,Yuan, Mengke,Yan, Dong-Ming,&Wu, Tieru.(2023).Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement.IEEE TRANSACTIONS ON MULTIMEDIA,25,6258-6271. |
| MLA | Xu, Jingzhao,et al."Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):6258-6271. |
入库方式： OAI收割
来源：自动化研究所
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