FDNet:用于红外和可见图像的端到端融合分解网络
Jing Di1, Li Ren1, Jizhao Liu2
1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, China.
PloS one
|September 18, 2023
概括
本研究介绍了融合分解网络 (FDNet),这是用于红外和可见图像融合的无监督方法. FDNet增强了融合图像中的纹理细节和对比度,提高了全天气候检测能力.
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:
- 红外和可见图像融合对于全天气候检测和其他在极端条件下需要清晰图像的任务至关重要.
- 现有的融合方法,通常是基于卷积神经网络 (CNN),努力充分利用突出物体和纹理特征,导致细节不足和对比度低的融合图像.
研究的目的:
- 提出一个无监督的,端到端的融合分解网络 (FDNet),用于红外和可见图像的融合.
- 通过增强合图像中的纹理细节和对比度来解决现有方法的局限性.
主要方法:
- 开发了FDNet,使用多级层,深度可分离的卷积,以及改进的卷积块注意模块 (I-CBAM) 来提取梯度和强度信息.
- 设计了特定的强度和渐变损失功能,包括改进的Frobenius强度损失规范和适应性重量块,以优化纹理信息.
- 包含单通道和双通道卷积层分解网络,以保存来自原始输入图像的详细信息.
主要成果:
- 拟议的FDNet方法与各种代表性图像融合技术相比,显示出更高的性能.
- 由FDNet生成的融合图像表现出增强的主观视觉质量,改进了纹理细节和对比度.
- 客观评估证实了先进的核聚变性能,表明了开发的网络和损失功能的有效性.
结论:
- FDNet为红外和可见图像融合提供了一种有效的无监督方法,克服了传统基于CNN的方法的局限性.
- 该网络能够提取和保存丰富的纹理和强度信息,从而显著提高了融合图像质量.
- 对于需要高保真图像融合的应用,FDNet具有前景,特别是在具有挑战性的环境监测和检测场景中.
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