边缘增强的红外图像超分辨率重建模型在变压器下
Lei Hu1, Long Hu2, MingHui Chen2
1School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China. hulei@jxnu.edu.cn.
Scientific reports
|July 6, 2024
概括
本研究介绍了基于变压器的TESR模型,用于增强红外图像分辨率. 它改善了边缘细节的恢复,减少了文物,从而为关键应用程序提供更好的超分辨率重建.
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:
- 红外图像的低分辨率阻碍了军事,安全和监视领域的应用.
- 现有的超级分辨率方法与边缘信息恢复和声文物作斗争.
研究的目的:
- 使用变压器架构提出一个边缘增强的红外图像超分辨率重建模型 (TESR).
- 为了应对在红外图像超分辨率中边缘细节损失和声效应的挑战.
主要方法:
- 集成了一个边缘检测辅助网络,以增强来自低分辨率输入的边缘信息.
- CSWin 变压器用于并行自我注意计算,扩展接收场,并利用更高层次的语义特征.
主要成果:
- 该TESR模型有效地提取全面的图像信息和更准确的边缘细节.
- 对于红外图像来说,可以获得增强的纹理细节和改进的超分辨率重建结果.
结论:
- 拟议的TESR模型在红外图像超分辨率方面取得了重大进展.
- 精确的边缘信息增强和文物减少是改善图像质量的关键贡献.
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