DCFNet:基于离散波纹转换和卷积神经网络的红外和可见图像融合网络
Dan Wu1, Yanzhi Wang1, Haoran Wang1
1School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710312, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种新的红外和可见光图像融合算法,使用离散波波变换 (DWT) 和卷积神经网络 (CNN). 该方法在融合图像中增强了细节和目标可见性,优于现有技术.
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:
- 当前的图像融合算法与缺失的细节,模糊的目标信息和视觉质量差的困扰.
- 红外和可见光图像融合对于需要全面的场景理解的应用至关重要.
研究的目的:
- 开发一种先进的红外和可见光图像融合算法,解决现有方法的局限性.
- 为了提高目标信息的清晰度和融合图像的整体视觉质量.
主要方法:
- 拟议的算法将离散波形变换 (DWT) 和卷积神经网络 (CNN) 集成到一个自编码器骨干中.
- DWT和反向DWT (IDWT) 层优化频域特征提取和重建.
- 整合了瓶残留块和协调注意力机制,以增强特征特征.
- 采用l1-规范融合策略和加权损失函数 (像素,梯度,结构损失) 进行网络优化.
主要成果:
- 拟议的算法有效地融合了红外和可见光图像,通过增强的场景信息产生了更清晰的结果.
- 对公共数据集的实验评估表明,在主观和客观指标上都表现出卓越的表现.
- 一般化实验证实了网络适应各种图像数据的强大能力.
结论:
- 开发的基于DWT-CNN的融合算法通过保留详细信息和提高目标可见性,显著提高了图像融合质量.
- 该方法提供了视觉上自然和和的融合图像,验证了其有效性和概括能力.
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