Knowledge Commons of Institute of Automation,CAS
Learning to Decompose and Restore Low-light Images with Wavelet Transform | |
Pengju Zhang1![]() ![]() ![]() | |
2021 | |
会议名称 | In ACM Multimedia Asia (MMAsia ’21) |
会议日期 | 2021-12-1 |
会议地点 | Gold Coast, Australia |
摘要 | Low-light images often suffer from low visibility and various noise. Most existing low-light image enhancement methods often amplify noise when enhancing low-light images, due to the neglect of separating valuable image information and noise. In this paper, we propose a novel wavelet-based attention network, where wavelet transform is integrated into attention learning for joint low-light enhancement and noise suppression. Particularly, the proposed wavelet-based attention network includes a Decomposition-Net, an Enhancement-Net and a Restoration-Net. In Decomposition-Net, to benefit denoising, wavelet transform layers are designed for separating noise and global content information into different frequency features. Furthermore, an attention-based strategy is introduced to progressively select suitable frequency features for accurately restoring illumination and reflectance according to Retinex theory. In addition, Enhancement-Net is introduced for further removing degradations in reflectance and adjusting illumination, while Restoration-Net employs conditional adversarial learning to adversarially improve the visual quality of final restored results based on enhanced illumination and reflectance. Extensive experiments on several public datasets demonstrate that the proposed method achieves more pleasing results than state-of-the-art methods. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47452 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
通讯作者 | Chaofan Zhang; Zheng Rong; Yihong Wu |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Pengju Zhang,Chaofan Zhang,Zheng Rong,et al. Learning to Decompose and Restore Low-light Images with Wavelet Transform[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ACM-MMA2021.pdf(5186KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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