论文标题
对光子有效成像的深域对抗适应
Deep Domain Adversarial Adaptation for Photon-efficient Imaging
论文作者
论文摘要
单光子光检测和范围（LIDAR）的光子有效成像可捕获场景的三维（3D）结构，每个像素仅几个检测到的信号光子。但是，现有的光子效率成像的计算方法在受限的方案上进行了预先调整或在模拟数据集上训练的。当应用于逼真的方案，其信噪比（SBR）和其他硬件特定属性与原始任务的属性不同时，模型性能通常会显着恶化。在本文中，我们提出了一种域对抗适应设计，以通过利用未标记的现实世界数据来减轻这种域转移问题，并节省大量资源。该方法证明了使用我们自制的上转换单光子成像系统在模拟和现实世界实验上的卓越性能，该系统提供了一种有效的方法来绕过缺乏实现现实应用计算成像算法的地面深度信息。
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel. However, the existing computational methods for photon-efficient imaging are pre-tuned on a restricted scenario or trained on simulated datasets. When applied to realistic scenarios whose signal-to-background ratios (SBR) and other hardware-specific properties differ from those of the original task, the model performance often significantly deteriorates. In this paper, we present a domain adversarial adaptation design to alleviate this domain shift problem by exploiting unlabeled real-world data, with significant resource savings. This method demonstrates superior performance on simulated and real-world experiments using our home-built up-conversion single-photon imaging system, which provides an efficient approach to bypass the lack of ground-truth depth information in implementing computational imaging algorithms for realistic applications.
