CDFFusion:一个无色偏差的融合网络,用于夜间红外和可见图像
Hao Chen1, Tinghua Zhang1, Shijie Zhai1
1Department of Electronic and Optical Engineering, Space Engineering University, Beijing 101416, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了CDFFusion,这是一种用于低光条件的新型红外和可见图像融合方法. 它有效地增强黑暗区域而不会过度曝光,改善了融合图像中的信息表达.
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:
- 红外和可见图像的融合旨在将互补的信息结合起来,以增强图像表示.
- 现有的融合方法在低光条件下扎,导致过度曝光,阻塞和颜色偏差等问题.
研究的目的:
- 提出CDFFusion,一种专门为低光场景设计的新型图像融合方法.
- 解决现有方法在提取暗细节和防止视觉工件方面的局限性.
主要方法:
- 使用Retinex理论将可见光图像分解为照明和反射组件.
- 融合了反射特征与红外特征,以产生融合图像的Y元件.
- 应用颜色映射公式来调整Cb和Cr组件,然后连接为最终的融合图像.
主要成果:
- 在LLVIP数据集上实现了卓越的性能,具有SF (17.6531) 和MS-SSIM (0.8335) 等指标.
- 有效地减轻视觉过度曝光和图像屏蔽效应.
- 在评估的方法中显示最小的颜色偏差.
结论:
- 在低光环境中,CDFFusion为红外和可见图像融合提供了有效的解决方案.
- 该方法成功地增强了信息表达,同时保持了图像保真性和颜色准确性.
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