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具有纹理恢复和物理约束的生成对抗网络用于远程传感图像解

Yanfei Jia¹, Wenshuo Yu², Liquan Zhao³

¹College of Electrical and Electronic Information Engineering, Beihua University, Jilin, 132013, China.

Scientific reports

|December 29, 2024

概括

一种新的方法,TRPC-GAN,有效地从遥感图像中去除大气雾. 这种技术保留了图像纹理和物理特征,提高了图像识别和分类准确度.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
图像处理图像处理

背景情况:

遥感图像中的大气雾降低了图像质量.
这种退化严重影响图像识别和分类任务.
现有的除气方法往往难以保持质地和物理精度.

研究的目的:

提出一种新的生成对抗网络,TRPC-GAN,以有效地消除雾.
为了提高视觉质量,并保留远程传感图像中的纹理信息.
为了提高后续图像识别和分类的准确性.

主要方法:

一个生成对抗网络 (TRPC-GAN) 结合了纹理恢复和物理约束.
多尺度模块用于在不同的受体场中捕获图像特征.
注意模块专注于关键特征信息.
一个多层次的对抗网络,用于恢复全球和本地图像信息.
物理约束损失的功能是保持图像的物理特征.

主要成果:

TRPC-GAN有效地消除了远程传感图像中的雾.
该方法证明了对原始图像纹理的优越保存.
增强视觉质量和物理特征保留在dehazed图像中.
实验结果显示,TRPC-GAN在除性能方面优于其他四种方法.

结论:

TRPC-GAN在遥感图像中提供了显著的进步.
拟议的方法提高了图像质量,以改善下游应用.
TRPC-GAN成功地平衡了雾去除与纹理和物理性质的保存.

关键词:

深度学习是一种深度学习.生成性的对抗性网络.遥感是一种远程传感.纹理恢复和物理限制.

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