使用基于Y网络的提取器和全球-本地区分器的图像融合
Danqing Yang1, Naibo Zhu2, Xiaorui Wang1
1School of Optoelectronic Engineering, Xidian University, Xi'an, 710071, China.
Heliyon
|May 24, 2024
概括
这项研究介绍了一种基于GAN (生成对抗网络) 的新方案,用于红外和可见图像融合. 该方法有效地提取和保存多尺度的特征,显著增强融合图像中的信息内容,扭曲最小.
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:
- 深度学习图像融合方法在从各种源图像中提取和保存信息丰富的特征方面面临着挑战.
- 现有的技术往往在尽量减少化输出中的扭曲方面扎.
研究的目的:
- 为红外和可见图像融合开发一个先进的基于生成对抗网络 (GAN) 的方案.
- 改进多尺度特征的提取和保存,以提高合图像质量.
主要方法:
- 使用Y-Net架构作为生成器骨干,结合残余密集块 (RDblocks) 进行多尺度表示学习.
- 实现了具有上下文关注的跨模式快捷方式 (CMSCA),用于选择性特征聚合.
- 采用统一的全球-本地歧视架构 (结合全球GAN和PatchGAN) 进行详细的差异检测.
主要成果:
- 拟议的方法有效地学习了歧视性的多尺度表示,从而产生更现实的融合图像.
- CMSCA促进了信息丰富的融合图像的构建,并改进了视觉效果.
- 全球-局部区分器增强了发电机捕获局部辐射和全球细节的能力,在没有明确规则的情况下实现了融合.
结论:
- 与最先进的方法相比,开发的基于GAN的融合方案在有意义的信息保存方面表现出卓越的性能.
- 多尺度特征提取和全球局部区分器的集成为红外和可见图像融合提供了强大的解决方案.
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