Abstract

Reconstructing a 3D image from the photon echo is a challenging task due to spurious detections associated with large amounts of background counts. Here, we propose a robust method for estimating the depth and reflectivity by using regularization by the denoising method, where the block matching and the 3D filtering are adopted as denoisers, and in the meantime, the steepest-descent method is implemented to solve the optimization problem. Experimental data with different signal-to-background ratios and different numbers of photons verify that our method is able to accurately recover 3D images. Compared with other existing methods, such as the maximum likelihood estimation algorithm, the photon efficient algorithm by Shin et al. [IEEE Trans. Comput. Imaging 1, 112 (2015) [CrossRef]  ], and the ManiPoP algorithm, our method can effectively remove noise while preserving the edge information of depth images, with better depth image estimation and smaller root mean square error, especially at low signal-to-noise ratios. The superiority of this method over other methods is verified on simulated data sets under different conditions.

© 2022 Optica Publishing Group

Dynamic single-photon 3D imaging with a sparsity-based neural network

Gongxin Yao, Yiwei Chen, Chen Jiang, Yixin Xuan, Xiaomin Hu, Yong Liu, and Yu Pan
Opt. Express 30(21) 37323-37340 (2022)

Deep-learning based photon-efficient 3D and reflectivity imaging with a 64 × 64 single-photon avalanche detector array

Xu Yang, ZiYi Tong, PengFei Jiang, Lu Xu, Long Wu, Jiemin Hu, Chenghua Yang, Wei Zhang, Yong Zhang, and Jianlong Zhang
Opt. Express 30(18) 32948-32964 (2022)

Robust photon-efficient imaging using a pixel-wise residual shrinkage network

Gongxin Yao, Yiwei Chen, Yong Liu, Xiaomin Hu, and Yu Pan
Opt. Express 30(11) 18856-18873 (2022)

3D photon counting integral imaging by using multi-level decomposition

Jaehoon Lee, Myungjin Cho, and Min-Chul Lee
J. Opt. Soc. Am. A 39(8) 1434-1441 (2022)

Polarization prior to single-photon counting image denoising

Xin Tian, Wei Chen, Zhongyuan Wang, and Jiayi Ma
Opt. Express 29(14) 21664-21682 (2021)