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红外和可见图像融合使用GAN与模糊逻辑和哈里斯霍克斯优化优化
Mahvash Zarimeidani1, Amir Amirabadi2, Nasrin Amiri1
1Department of Electrical Engineering, ST.C, Islamic Azad University, Tehran, Iran.
Scientific reports
|December 2, 2025
概括
这项研究引入了一个带有哈里斯·霍克斯优化 (HHO) 的模糊生成对抗网络 (FGAN),以实现卓越的可见和红外图像融合. 这种新的方法提高了监视和医学诊断等应用的适应性和视觉质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 可见和红外图像融合对于监视和医学诊断等应用至关重要.
- 现有的核聚变方法在适应性和性能方面存在局限性.
- 机器学习技术在图像融合中越来越重要.
研究的目的:
- 为可见和红外图像融合提出一个与哈里斯霍克斯优化 (HHO) 集成的新型模糊生成对抗网络 (FGAN).
- 通过动态优化Mamdani型模糊逻辑系统来解决现有方法的局限性.
- 通过混合方法实现卓越的适应性和融合精度.
主要方法:
- 使用HHO调整模糊规则和输出会员功能,使用,PSNR和SSIM作为训练目标.
- 用一个支持向量机器 (SVM) 与Frechet初始距离 (FID) 进行歧视者训练.
- 开发了一个混合框架,结合了模糊逻辑,HHO和生成对抗网络 (GAN).
主要成果:
- 拟议的FGAN在红外和可见图像融合中表现出了显著的适应性.
- 与最先进的技术相比,实现了卓越的视觉质量和融合精度.
- 实验结果显示PSNR高达55dB,SSIM高达0.99,并在TNO数据集上SF低于10.
结论:
- 新的FGAN方法为图像融合提供了一个革命性的范式.
- 该方法显著提高了合图像的清晰度和信息保留.
- 这种技术具有很大的潜力,可以改善遥感,监视和医疗诊断.
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