通过子窗口差异过器和加权最小正方形优化,突出度增强的红外和可见图像融合
Peicheng Wang1, Tingsong Li2, Pengfei Li3
1School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, China.
PloS one
|July 7, 2025
概括
本研究引入了一种新的红外和可见图像融合 (IVIF) 方法,使用子窗口差异过器和加权最小平方优化. 这种新的方法在没有深度学习的情况下增强了突出的特征和纹理细节,超过了现有的技术.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 信号处理 信号处理
背景情况:
- 现有的红外和可见图像融合 (IVIF) 方法难以增强突出特征和视觉清晰度.
- 对IVIF的深度学习方法需要大量的培训数据,并可能导致融合图像扭曲.
研究的目的:
- 开发一种新的IVIF方法,有效地增强突出特征并保留纹理细节.
- 提供高质量的融合技术,不依赖于大规模的训练数据集或地面真相.
主要方法:
- 使用子窗口差异过器 (SVF) 将图像分解成不同的带层,将突出的特征和纹理分开.
- 采用基于加权最小方程优化 (WLSO) 的突出地图测量方案来计算重量地图.
- 使用像素级总和重建融合图像,以增强功能地图表示.
主要成果:
- 拟议的IVIF方法在定性和定量评估中表现优于九种最先进的技术.
- 在突出目标突出显示和纹理细节保存方面取得了显著的改进.
- 在三个公共数据集上表现优于现有方法.
结论:
- 新的IVIF方法为基于深度学习的方法提供了有效的替代方案,在没有广泛的培训的情况下提供高质量的融合图像.
- 该技术在增强重要特征和保持图像清晰度方面表现出色.
- 未来的工作将集中在适应性参数优化和复杂场景的深度学习集成上.
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