DFFNet:一个双域特征融合网络,用于单一远程传感图像处理
Huazhong Jin1,2, Zhang Chen1, Zhina Song1,2
1School of Computer and Information Engineering, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了双域特征融合网络 (DFFNet),以改进远程传感图像脱雾. 网络有效地消除大气雾,同时还原图像细节,在具有挑战性的条件下提高性能.
科学领域:
- 遥感技术
- 计算机视觉
- 图像处理
背景情况:
- 对于下游任务来说,单一的远程传感图像消毒至关重要.
- 传统的方法与不均的雾和细节恢复作斗争.
- 大气散射会显著降低图像质量.
研究的目的:
- 为单个遥感图像提供有效的方法.
- 解决平衡雾清除和细节恢复现有方法的局限性.
- 为了提高遥感图像分析的性能.
主要方法:
- 开发了一个双域特征融合网络 (DFFNet),包括一个频率恢复单元 (FRU) 和一个上下文提取单元 (CEU).
- FRU适应调节低频幅度以抑制频域中的雾.
- 为了提供上下文信息和详细的重建指导,CEU提取了多尺度的空间特征.
- 集成了一个双域特征融合模块 (DDFFM) 与一个注意力机制来融合FRU和CEU的特征.
主要成果:
- DFFNet在整个图像中展示了有效的雾抑制.
- 网络成功地重建了被密的雾所削弱的细节.
- 在StateHaze1k,RICE和RRSHID数据集的实验结果显示出具有竞争力的表现.
- 与现有方法相比,实现了优越的视觉质量和定量指标.
结论:
- DFFNet为单个遥感图像提供了一个强大的解决方案.
- 这种双域方法有效地平衡了雾清除和细节保存.
- 拟议的方法显著提升了遥感图像增强的最新技术.
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