Lensless Three-Dimensional Imaging under Photon-Starved Conditions

Abstract

In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.

Three-Dimensional Image Visualization under Photon-Starved Conditions Using N Observations and Statistical Estimation

“…In addition, 3D imaging techniques may not provide 3D information of objects under photon-starved conditions due to the lack of photons. To obtain 3D information of objects under these conditions, photon-counting integral imaging has been studied [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”

A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data

“…To address these issues, the ICESat-2, equipped with the advanced topographic laser altimeter system (ATLAS) [ 9 ], was launched and put into operation in September 2018. In contrast to the full waveform LiDAR system from the GLAS, ATLAS is a photon-counting LiDAR that utilizes high-frequency pulses [ 10 ] with an emission energy of 160/40 μj at 10 kHz and 0.7 m along-track resolution for precise surface measurements [ 11 , 12 ], providing a high sampling rate, a small footprint size (10 m), and a nearly continuous along-track strip of point cloud data [ 13 ]. However, the original data from ATLAS, particularly the daytime data, are heavily contaminated by background noise arising from solar radiation and dark current returns [ 14 ] and feature a random and wide distribution.…”