Fast Computational Periscopy in Challenging Ambient Light Conditions through Optimized Preconditioning
Fast Computational Periscopy in Challenging Ambient Light Conditions through Optimized Preconditioning
复制标题
通过优化预处理在具有挑战性的环境光条件下快速计算潜望镜
DOI：
10.1109/iccp51581.2021.9466264
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Goyal, Vivek K
中科院分区：
文献类型：
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作者：
Saunders, Charles;Goyal, Vivek K
非视距(NLOS)成像是一种快速发展的技术，它提供了不对称的视觉：看到而不被看到。尽管与主动方法相比，被动方法在精度、分辨率和深度恢复方面有限，但被动方法的能力尤其令人惊讶，因为它们通常只使用一台廉价的数码相机。被动非视距成像的最大挑战之一是环境背景光，它限制了测量的动态范围，同时没有携带关于场景隐藏部分的有用信息。在这项工作中，我们提出了一种新的重建方法，该方法使用优化的线性变换来平衡对非信息光的抑制和信息光的保留，从而在高环境光条件下从空白墙壁的照片中快速(视频率)地重建隐藏的场景。
Non-line-of-sight (NLOS) imaging is a rapidly advancing technology that provides asymmetric vision: seeing without being seen. Though limited in accuracy, resolution, and depth recovery compared to active methods, the capabilities of passive methods are especially surprising because they typically use only a single, inexpensive digital camera. One of the largest challenges in passive NLOS imaging is ambient background light, which limits the dynamic range of the measurement while carrying no useful information about the hidden part of the scene. In this work we propose a new reconstruction approach that uses an optimized linear transformation to balance the rejection of uninformative light with the retention of informative light, resulting in fast (video-rate) reconstructions of hidden scenes from photographs of a blank wall under high ambient light conditions.