交互式残余协调注意力和对比学习,用于在三重频段中的红外和可见图像融合
Zhihua Xie1,2, Sha Zong3, Qiang Li3
1Key Lab of Optic-Electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang, China. xie_zhihua68@aliyun.com.
Scientific reports
|January 3, 2024
概括
本研究引入了一种使用频域分析和注意力机制的新型红外和可见图像融合模型. 拟议的方法提高了细节的保存和信息互补性,以获得卓越的融合结果.
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:
- 基于自动编码器 (AE) 的模型在红外和可见图像融合方面表现有前途.
- 关键的挑战包括编码过程中的信息丢失和简单的融合策略.
研究的目的:
- 开发一个先进的红外和可见图像融合模型.
- 克服现有的基于AE的核聚变方法的局限性.
主要方法:
- 频域转换为高频,中频和低频子频段.
- 集成一个可学习的坐标注意模块,用于适应融合.
- 应用对比学习来增强多层次信息互补性.
主要成果:
- 该模型有效地融合了红外和可见图像,保留了目标亮点和可读细节.
- 定性和定量分析证实了比最先进的方法更高的性能.
结论:
- 拟议的交互式剩余注意力融合策略和对比学习方法显著改善了红外和可见图像融合.
- 该模型在生成高质量的融合图像方面表现出强大的性能,具有增强的细节可维护性.
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