基于对抗性转移学习的轻量级红外图像否定方法.
Wen Guo1,2, Yugang Fan1,2, Guanghui Zhang1,2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了一种轻量级的红外图像消暗方法,使用对抗转移学习和生成对抗网络 (GAN). 这种方法有效地消除了噪音,通过改进的特征提取和减少复杂性来提高图像质量.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 由于数据有限,红外图像往往会受到噪声的影响.
- 现有的脱色方法可能因样品品种不够多而难以应对.
研究的目的:
- 提出一种轻量级和高效的红外图像消光方法.
- 利用对抗转移学习来克服红外成像中的数据限制.
主要方法:
- 使用了一个生成对抗网络 (GAN) 框架.
- 采用分阶段转移学习策略:对可见光数据进行预训练,然后对红外数据进行微调.
- 集成的结构重组参数化,边缘卷积和渐进式多尺度注意区块 (PMAB) 用于增强特征提取.
主要成果:
- 该方法有效地从红外图像中去除添加式白色高斯噪声.
- 在公共和现实世界数据集上表现出卓越的泄露性能.
- 实现了模型参数和复杂性的显著减少,以提高效率.
结论:
- 提出的对抗性转移学习方法为红外图像消噪提供了强大的解决方案.
- 集成先进的网络组件增强了边缘和纹理特征识别.
- 轻量级的架构确保了高效的消噪,适合实际应用.
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