The role of Wigner Distribution Function in Non-Line-of-Sight Imaging

Xiaochun Liu, A. Velten
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引用次数: 9

Abstract

Non-Line-of-Sight imaging has been linked to wave diffraction by the recent phasor field method. In wave optics, the Wigner Distribution Function description for an optical imaging system is a powerful analytical tool for modeling the imaging process with geometrical transformations. In this paper, we focus on illustrating the relation between captured signals and hidden objects in the Wigner Distribution domain. The Wigner Distribution Function is usually used together with approximated diffraction propagators, which is fine for most imaging problems. However, these approximated diffraction propagators are not valid for Non-Line-of-Sight imaging scenarios. We show that the exact phasor field propagator (Rayleigh-Sommerfeld Diffraction) does not have a standard geometrical transformation, as compared to approximated diffraction propagators (Fresnel, Fraunhofer diffraction) that can be represented as shearing or rotation in the Wigner Distribution Function domain. Then, we explore differences between the exact and approximated solutions by characterizing errors made in different spatial positions and acquisition methods (confocal, non-confocal scanning). We derive a lateral resolution based on the exact phasor field propagator, which can be used as a reference for theoretical evaluations and comparisons. For targets that lie laterally outside a relay wall, the loss of resolution is geometrically illustrated in the context of the Wigner Distribution Function.
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维格纳分布函数在非视距成像中的作用
最近的相量场法将非视距成像与波衍射联系起来。在波动光学中,光学成像系统的维格纳分布函数描述是用几何变换对成像过程进行建模的有力分析工具。在本文中,我们着重说明捕获信号和隐藏目标在维格纳分布域中的关系。维格纳分布函数通常与近似衍射传播函数一起使用,这对于大多数成像问题都是很好的。然而,这些近似的衍射传播算子不适用于非视距成像场景。我们表明,与近似的衍射传播子(菲涅耳衍射、弗劳恩霍夫衍射)相比,精确的相量场传播子(瑞利-索默菲尔衍射)没有标准的几何变换,而近似的衍射传播子(菲涅耳衍射)可以在维格纳分布函数域中表示为剪切或旋转。然后,我们通过表征在不同空间位置和采集方法(共聚焦、非共聚焦扫描)下产生的误差来探讨精确解和近似解之间的差异。我们推导了一个基于精确相量场传播子的横向分辨率,可以作为理论评价和比较的参考。对于横向位于中继墙外的目标,分辨率的损失在维格纳分布函数的背景下以几何方式说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Awards [3 award winners] NLDNet++: A Physics Based Single Image Dehazing Network Action Recognition from a Single Coded Image Fast confocal microscopy imaging based on deep learning Comparing Vision-based to Sonar-based 3D Reconstruction
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