SGLE-Net:通过强度-斯托克斯域信息融合网络,在低光条件下提升极化意识
Applied optics
|August 12, 2025
概括
这项研究介绍了SGLE-Net,这是一个用于增强低光偏光图像的新型网络. 它有效地提高了图像强度和偏振细节,如偏振度 (DoP) 和偏振角度 (AoP).
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 光学是什么?光学是什么?光学是什么?
背景情况:
- 极化摄像头捕获RGB以外的丰富的物理信息,但在低光条件下,图像质量会降低.
- 低光偏振成像挑战包括减少光强度的捕获和偏振参数的不准确提取,如偏振度 (DoP) 和偏振角度 (AoP).
- 现有的一般图像增强方法不适合极化图像,因为它们具有独特的信息内容.
研究的目的:
- 开发一个专门的深度学习网络,SGLE-Net,用于增强低光极化图像.
- 为了有效地利用强度和偏振域信息来提高图像质量.
- 为了解决低光偏振成像当前方法的局限性.
主要方法:
- 拟议的SGLE-Net是一个以斯托克斯为指导的增强网络,有两个子网络:强度增强网络和极性合并网络.
- 引入了基于斯托克斯的融合增强模块,以指导强度和极化特征的融合.
- 开发了一个极关节目标功能,将强度和极化损失结合起来,以实现平衡的训练.
主要成果:
- 与现有方法相比,SGLE-Net在低光偏光图像数据集上表现出优异的性能.
- 该网络有效地提高了图像的总强度.
- 提取了更高质量的极化度 (DoP) 和极化角度 (AoP) 信息.
结论:
- 通过整合强度和偏振信息,SGLE-Net提供了一种有效的解决方案,用于增强低光偏振图像.
- 拟议的方法提高了图像强度和极化参数的质量.
- 这项工作推进了极化成像在具有挑战性的低光环境中的功能.
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