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AFMNet:一个双域协作网络,具有用于低光图像增强的频率预先指导
1School of Computer Engineering, Shenyang Jianzhu University, Shenyang 110168, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
AFMNet通过结合空间和频率域处理来增强低光图像. 这种双域方法提高了亮度,并保留了细节,以获得更好的图像质量.
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:
- 低光图像增强 (LLIE) 面临着诸如照明不良,遮蔽和噪音等挑战.
- 现有的方法难以平衡亮度增强与细节保存.
- 频域分析显示在降解的图像中抑制的振幅和稳定的相.
研究的目的:
- 提出AFMNet,一个新的双域网络用于低光图像增强.
- 为了提高照明规范化和细节恢复,利用信息理论频率.
- 为了实现亮度校正和结构信息保存之间的平衡.
主要方法:
- 开发了AFMNet,这是一个在空间和频率领域运行的协作增强网络.
- 引入了适应幅度调制图的多尺度幅度估计器 (MSAE).
- 采用双分支光谱空间注意力 (DBSSA) 模块与频率调制注意力块 (FMAB) 和可变深度注意力块 (SVDAB).
- 使用光谱门前网络 (SGFFN) 进行带宽选择性增强.
主要成果:
- AFMNet有效地通过使用之前的频率调整全球照明.
- 空间分支恢复局部细节,保存结构信息.
- 双域方法在照明校正和细节保存之间实现了有利的平衡.
- AFMNet在多个低光增强基准上展示了最先进的性能.
结论:
- 通过整合空间和频率域处理,AFMNet提供了一个强大的解决方案,用于低光图像增强.
- 建议的信息理论频率先指导网络取得卓越的结果.
- 在平衡亮度增强和细节保真方面,AFMNet实现了卓越的性能.
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