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极化图像拆解和RGB图像增强用于色彩极化稀疏焦平面阵列阵列

Ju Liu, Jin Duan, Youfei Hao

|July 21, 2023

概括

这项研究引入了一种新的焦平面 (DoFP) 极化传感器设计的稀疏划分. 它通过提高光子传输和数据保真性,使同时实现高分辨率偏振和RGB成像.

科学领域:

光学和光子学在光学和光子学.
图像处理图像处理
计算机视觉计算机视觉

背景情况:

传统的焦平面分色 (DoFP) 极化传感器由于叠加的贝耶尔和极化过器而遭受低光子传输和信息保真.
现有的方法通常依赖于极化强度插值,这可能引入噪声并降低图像质量.

研究的目的:

开发一个稀疏的DoFP极化传感器结构,用于同时进行高分辨率极化和RGB图像采集.
提出一种新的稀疏斯托克斯向量完成方法,以改善极化信息恢复.
引入一个进步的模型,Sparse-PDM,用于高质量的极化和RGB图像生成.

主要方法:

模拟焦平面偏振传感器结构的稀疏划分,以优化波器分布.
开发一种稀疏的Stokes向量完成方法,直接映射S1和S2组件,避免噪声放大.
实施稀疏偏度图像解 (Sparse-PDM) 模型,结合RGB文物删除和稀疏偏度图像完成.

主要成果:

拟议的稀疏传感器结构在极化和RGB图像质量之间实现了更好的平衡.
稀疏的斯托克斯向量完成方法有效地恢复了极化信息,降低了噪音.
实验结果表明,Sparse-PDM模型在多个数据集上优于现有的最先进方法.

结论:

新型稀疏的DoFP传感器设计和Sparse-PDM模型可以同时获取高质量的极化和RGB图像.
这种方法克服了传统DoFP传感器的局限性,提供了更好的光子传输和数据保真性.
该方法为先进的极化成像应用提供了强大的解决方案.

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