多模式图像融合网络,具有预先引导的动态降解去除,用于极端环境感知
Yi Li1, Donghui Li1, Shu Fang2
1Tianjin University, School of Electrical and Information Engineering, Tianjin, 300072, China.
Scientific reports
|November 18, 2025
概括
本研究引入了一种自适应的多式联动图像融合技术,以提高极端天气造成的图像退化程度的清晰度. 该方法在具有挑战性的条件下改善了融合图像质量和细节表示.
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:
- 多模式图像融合整合了多样化的图像数据,以提高清晰度,这对于环境监测和夜视等应用至关重要.
- 现有的融合方法在现实场景中经常会因为恶劣天气造成的图像退化而失败,导致融合图像的清晰度和细节差.
- 复杂环境中的退化图像对有效的信息提取和融合构成重大挑战.
研究的目的:
- 开发一种适应性的多式联通图像融合技术,专门设计用于极端和退化环境场景.
- 为了解决当前融合方法在处理雾,低光和过度曝光等因素引起的图像退化方面的局限性.
- 为了提高合图像在具有挑战性的现实环境中的清晰度,细节表示和整体性能.
主要方法:
- 使用物理参数的预增强模块通过过干扰来自适应地增强退化输入图像.
- 一个基于网关的稀疏专家混合机制,以大型视觉语言模型生成的文本描述为指导,创建了一个动态的网络结构.
- 复合损失函数将像素级,梯度,重建和相互信息损失结合起来,以提高融合性能.
主要成果:
- 与主流的融合技术相比,拟议的方法在多个公共数据集上显示出明显优异的性能.
- 该技术有效地处理各种降解场景,包括雾,低光和过度曝光,从而提高图像清晰度.
- 定量指标证实了在细节保留和融合图像的模式歧视方面提高的性能.
结论:
- 开发的自适应多式联动图像融合技术为极端和退化环境中的成像提供了强大的解决方案.
- 适应性预增强,动态稀疏网络和复合损失函数的整合导致了最先进的融合性能.
- 这种方法增强了图像融合技术在挑战现实世界监控和监控任务中的实际应用性.
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