大小规模结构混合U-Net用于亮化低光图像
Hao Cheng1, Kaixin Pan1, Haoxiang Lu1
1School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究引入了一种新的双分支网络,用于在低光下增强图像. 拟议的方法有效地增加图像的亮度,同时改善色彩校正和细节恢复,优于现有的技术.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 现有的低光图像增强方法经常在细节恢复和准确的色彩校正方面扎.
- 在低照明条件下提高视觉质量仍然是图像处理中的一个重大挑战.
研究的目的:
- 开发一种新的双分支网络,用于在低光下优异的图像增强.
- 为了解决当前方法中存在的细节增强和色彩校正方面的局限性.
主要方法:
- 一个双分支网络,包括一个色彩校正网络 (CC-Net) 和一个增光网络 (LB-Net).
- 使用CIELAB颜色空间进行亮度和颜色组件提取.
- 实施CC-Net的U形网络和LB-Net的大小规模结构,以探索多个规模的功能.
- 整合一个高效的功能交互模块,用于跨行业信息交换.
主要成果:
- 拟议的方法在低照明场景中显著提高了图像亮度,细节和颜色保真度.
- 公共基准的实验结果显示,与最先进的低光增强技术相比,性能优越.
- 在低光条件下,对象检测性能得到了明显的改进.
结论:
- 双分支网络有效地解决了在低光图像中细节增强和色彩校正的挑战.
- 拟议的方法提供了一个强大的解决方案,以提高低亮度图像及其应用的质量.
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