基于三级分解和ResNet特征转移的红外和可见图像的融合
Jingyu Ji1, Yuhua Zhang1, Yongjiang Hu1
1Department of UAV, Army Engineering University, Shijiazhuang 050003, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种新的红外和可见图像融合算法,使用三级分解和ResNet特征转移. 该方法通过改进特征提取和信息整合来提高目标识别,以获得更好的视觉和定量结果.
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:
- 现有的图像融合算法在与红外和可见图像的不完整分解和次优特征提取方面扎.
- 准确的目标识别依赖于来自融合红外和可见图像的综合数据.
研究的目的:
- 提出一个先进的红外和可见图像融合算法,解决当前方法的局限性.
- 通过改进的图像融合技术,提高目标识别的准确性.
主要方法:
- 一种新的三尺度分解方法,用于精细分层的源图像.
- 一种优化的加权最小方程 (WLS) 方法来融合能量层,结合红外能量和可见细节.
- ResNet-特征转移用于细节层的融合,以提取更深的轮结构.
- 结构层融合的加权平均策略.
主要成果:
- 与五种现有方法相比,拟议的算法在视觉质量和定量评估方面都表现出卓越的性能.
- 实现了红外能量信息和可见图像细节的有效融合.
- 增强了详细特征的提取,包括更深的轮结构.
结论:
- 开发的融合算法对红外和可见图像融合的现有技术提供了显著的改进.
- 该方法为准确的目标识别和图像处理提供了更可靠和更全面的数据表示.
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