SCFusion:基于突出的补偿的红外和可见融合
Haipeng Liu1, Meiyan Ma1, Meng Wang1,2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种基于突出补偿的新型图像融合方法,用于在低光条件下增强红外和可见图像. 该技术改善了目标特征描述和全球场景感知,优于现有的融合算法.
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:
- 目前的深度学习融合方法与低光红外图像作斗争,导致细节和对比度差.
- 现有的算法往往无法充分利用红外和可见模式的互补信息.
研究的目的:
- 通过有效地整合红外和可见图像特征,开发用于低光条件的优质图像融合方法.
- 为了解决当前融合技术在纹理细节,目标对比度和视觉感知方面的局限性.
主要方法:
- 提出了一种基于突出补偿的融合方法,使用多尺度边缘梯度模块 (MEGB) 来提取纹理.
- 引入了一个突出密度残余模块 (SRDB) 用于突出地图生成和特征提取,以突出损失进行训练.
- 实现空间偏差模块 (SBM) 以有效地融合全球和本地图像信息.
主要成果:
- 广泛的实验表明,与现有方法相比,在描述目标特征和全球场景方面具有显著的优势.
- 废弃性研究证实了拟议模块 (MEGB,SRDB,SBM) 的有效性.
- 该方法显示了高级视觉任务的便利性,特别是语义细分.
结论:
- 提出的基于突出补偿的融合方法在低光场景中显著提高了图像质量.
- 新型模块有效地提取和融合多模式图像信息,改善特征表示.
- 这种方法为红外和可见图像融合提供了强大的解决方案,并有利于下游视觉任务.
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