许多光子中的几个光子：用于光子有效主动成像的信号和噪声分离
A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging
摘要：
传统的LIDAR系统需要数百或数千个光子检测来形成精确的深度和反射率图像。最近的光子高效的计算成像方法是非常有效的，只有1.0至3.0检测到的光子每像素，但他们没有证明在信号与背景比（SBR）低于1.0，因为他们的成像精度显着降低在存在高背景噪声。我们介绍了一种新的方法，深度和反射率估计，重点是从信号和噪声源的解混贡献。在图像中的每个像素处，自适应地确定并应用短持续时间范围门以去除可能是由于噪声引起的检测。对于检测太少而无法准确执行这种审查的像素，我们从相邻像素中借用数据来改善深度估计，其中邻域形成也自适应于场景内容。在不同的噪声水平的实验数据上证明了算法的性能。结果表明，反射率和深度估计的最先进的方法，特别是在低信号背景比的改进性能。特别是，准确的成像被证明与SBR低至0.04。这种光子效率高、噪声耐受性强的方法的验证证明了快速、远程和低功率LIDAR成像的可行性。
英文摘要：
Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected photons per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in the presence of high background noise. We introduce a new approach to depth and reflectivity estimation that focuses on unmixing contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, we borrow data from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content. Algorithm performance is demonstrated on experimental data at varying levels of noise. Results show improved performance of both reflectivity and depth estimates over state-of-the-art methods, especially at low signal-to-background ratios. In particular, accurate imaging is demonstrated with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant method demonstrates the viability of rapid, long-range, and low-power LIDAR imaging.
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