基于交互式注意力通道的图像脱雾变压器
Yanfei Chen1, Tong Yue1, Pei An2
1Hubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究介绍了一种基于变压器的新型模型,用于单个图像dehazing. 改进后的模型有效地融合了全球和本地特征,显著改善了计算机视觉中的雾清除和细节恢复.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 传统的dehazing算法在全球特征关联和局部细节保存方面遇到了困难.
- 单个图像dehazing对于从模糊输入中恢复清晰的场景至关重要.
研究的目的:
- 提出一种基于变压器的新型排气模型.
- 为了增强全球特征关联和局部细节保存在图像dehazing.
主要方法:
- 一个U形的编码器-解码器架构,具有交互式通道注意力.
- 多尺度特征金字塔用于多维信息提取.
- 改善了对不同雾密度的跨频道注意力.
主要成果:
- 实现了0.53dB (室内) 和1.64dB (室外) 的PSNR收益.
- 展示了1.4% (室内) 和1.7% (室外) 的SSIM改进.
- 成功恢复了精细的结构细节,并在密的雾中保持了颜色保真.
结论:
- 拟议的基于变压器的模型显著优于现有方法.
- 交互式道注意力机制有效地融合了全球和本地特征.
- 该方法提高了图像处理中的感知质量和定量准确性.
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