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学习时间复合的相位编码孔径用于快照光谱深度成像

Edwin Vargas, Hoover Rueda-Chacón, Henry Arguello

|December 2, 2023

概括

这项研究引入了一种用于联合光谱深度 (SD) 成像的新型时间复合编码光圈 (TMCA). 与现有方法相比,TMCA提高了深度和光谱重建质量.

科学领域:

计算机成像成像技术
光学工程的光学工程.
计算机视觉计算机视觉计算机视觉

背景情况:

光谱深度 (SD) 成像对于许多应用至关重要,但传统上被单独研究.
现有的单一快照SD成像依赖于光学调制 (编码) 和计算解码.
目前的光学调制方法包括编码的光圈,相罩和主动照明.

研究的目的:

为联合光谱深度成像提出一种新的光学调制策略.
为了提高单一快照SD成像系统的性能.
开发一个端到端的框架,集成光学调制和计算解码.

主要方法:

开发了一个使用颜色编码光圈 (CCA),时间变化的相位编码光圈和像素快门的时间复合编码光圈 (TMCA).
使用空间变量点扩散函数 (PSF) 来提高深度区分能力.
采用深度学习来实现光学调制和计算解码的端到端 (E2E) 联合学习.
杆CCA用于选择性频段过以编码光谱信息.

主要成果:

由于其空间变异的PSF,TMCA策略可以更好地恢复深度信息.
使用CCA和重建算法,光谱信息被有效地编码和解.
与最先进的SD成像技术相比,模拟和原型实验显示出更高的性能.
E2E深度学习框架成功地集成了光学编码和计算解码.

结论:

拟议的TMCA战略在单一快照光谱深度成像方面取得了重大进展.
通过深度学习共同学习光学调制和计算解码,提高了重建质量.
在光谱和深度估计方面,TMCA系统表现出强大的性能.

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