DFCNet:用于低光图像增强的双阶段频域校准网络
Hui Zhou1, Jun Li1, Yaming Mao1
1School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China.
Journal of imaging
|August 27, 2025
概括
一个新的双阶段频域校准网络 (DFCNet) 可以在不需要标记数据的情况下增强低光图像. 这种轻量级,无人监控的方法可提高关键应用的图像质量.
科学领域:
- 计算机视觉
- 图像处理
- 人工智能
背景情况:
- 低光条件会降低图像质量,影响监视和医疗诊断.
- 传统的低光图像增强 (LLIE) 方法通常需要大量的数据和大量的计算资源,这限制了实时应用.
研究的目的:
- 开发一种轻量级,无监督的LLIE方法,以改进实时图像处理.
- 解决传统的LLIE技术的局限性,包括数据依赖和计算成本.
主要方法:
- 提出了一个双阶段频域校准网络 (DFCNet),用于无监督的低光图像增强.
- 使用初步特征调制 (PFM) 模块和照明估计 (IE) 模块进行初始增强.
- 在训练过程中使用频域剩余校准 (FRC) 模块进行参数优化.
主要成果:
- 与现有方法相比,DFCNet在基准数据集上取得了更好的表现.
- 提出的方法产生了视觉上更清晰,更自然的增强图像.
- 在低光条件下改善图像质量指标的有效性.
结论:
- DFCNet提供了一种有效和高效的低光图像增强解决方案.
- 无监督,轻量级的方法使其适合实时应用.
- 双阶段频域校准显著提高图像质量和细节保存.
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