DSA-Net:通过双流不对称网络进行红外和可见图像融合
Ruyi Yin1, Bin Yang1, Zuyan Huang1
1College of Electrical Engineering, University of South China, Hengyang 421001, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了DSA-Net,这是一个用于红外和可见图像融合的新型双流不对称网络. 它增强了特征提取,改善了用于目标检测等任务的融合图像质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 红外和可见图像的融合对于场景的表征至关重要.
- 现有的深度学习融合方法通常使用对称网络,导致由于忽视的模式差异而导致信息丢失.
研究的目的:
- 提出一个新的融合框架,解决红外和可见图像的独特特征.
- 改进图像融合中的信息保存和特征提取.
主要方法:
- 设计了一个双流不对称网络 (DSA-Net),用于红外和可见图像,具有单独的特征提取器.
- 红外分支的集成变压器架构用于局部和上下文特征提取.
- 在可见的分支中使用剩余的密集块进行详细的背景和纹理提取.
主要成果:
- 在定性和定量评估中,DSA-Net在最先进的方法中表现优越.
- 融合结果在应用于目标检测任务时显示出有效性.
结论:
- 拟议的双流不对称网络通过利用专门的特征提取来有效地融合红外和可见图像.
- DSA-Net为下游应用 (如目标检测) 提供了更好的性能和潜力.
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