MGFusion:用于红外和可见图像融合的多式大型语言模型引导的信息感知
Zengyi Yang1, Yunping Li2, Xin Tang2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Frontiers in neurorobotics
|January 7, 2025
概括
这项研究引入了一种新的红外和可见图像融合方法,使用多式联络大型语言模型. 它通过利用语义信息来提高复杂场景中的图像质量,避免复杂的网络设计.
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:
- 现有的图像融合方法通常依赖于复杂的网络架构.
- 简单的融合策略在复杂的场景中扎,限制了性能.
- 需要有效整合语义信息以实现增强融合的方法.
研究的目的:
- 提出一种新的红外和可见图像融合方法,利用多式联络大型语言模型.
- 在复杂的环境中解决当前融合策略的局限性.
- 通过结合丰富的语义信息来提高图像质量.
主要方法:
- 为红外和可见图像融合开发了一种基于多式大型语言模型的方法.
- 引入了基于CLIP的信息注入 (CII) 来提取语义特征.
- 设计的CLIP引导特征融合 (CFF) 用于选择性特征集成.
主要成果:
- 拟议的方法有效地用语义信息来补充红外和可见特征.
- CFF策略成功地将复杂场景中的特征融合在一起,克服了现有的挑战.
- 实验结果表明,与现有的融合技术相比,其性能优越.
结论:
- 利用多式联通大型语言模型为复杂的网络设计提供了强大的替代方案,用于图像融合.
- 拟议的方法增强了特征表示和融合能力.
- 该方法在多个数据集中显示出显著的有效性和优越性.
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