2D signal estimation for sparse distributed target photon counting data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 May 2024
06 May 2024
Historique:
received:
14
09
2023
accepted:
23
04
2024
medline:
7
5
2024
pubmed:
7
5
2024
entrez:
6
5
2024
Statut:
epublish
Résumé
In this study, we explore the utilization of penalized likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.
Identifiants
pubmed: 38710756
doi: 10.1038/s41598-024-60464-1
pii: 10.1038/s41598-024-60464-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10325Subventions
Organisme : NASA
ID : NASA 21-NSTGRO22-0326
Pays : United States
Informations de copyright
© 2024. The Author(s).
Références
Hadfield, R. H. Single-photon detectors for optical quantum information applications. Nat. Photon 3, 696–705. https://doi.org/10.1038/nphoton.2009.230 (2009).
doi: 10.1038/nphoton.2009.230
Zoller, P. et al. Quantum information processing and communication. Eur. Phys. J. D 36, 203–228. https://doi.org/10.1140/epjd/e2005-00251-1 (2005).
doi: 10.1140/epjd/e2005-00251-1
Hsieh, S. S., Leng, S., Rajendran, K., Tao, S. & McCollough, C. H. Photon counting ct: Clinical applications and future developments. IEEE Trans. Radiat. Plasma Med. Sci. 5, 441–452. https://doi.org/10.1109/TRPMS.2020.3020212 (2021).
doi: 10.1109/TRPMS.2020.3020212
pubmed: 34485784
Hadfield, R. H. et al. Single-photon detection for long-range imaging and sensing. Optica 10, 1124–1141. https://doi.org/10.1364/OPTICA.488853 (2023).
doi: 10.1364/OPTICA.488853
Boksenberg, A. & Burgess, D. An image photon counting system for optical astronomy. Adv. Electron. Electron Phys. 33, 835–849. https://doi.org/10.1016/S0065-2539(08)60798-2 (2018).
doi: 10.1016/S0065-2539(08)60798-2
Rapp, J., Ma, Y., Dawson, R. M. A. & Goyal, V. K. Dead time compensation for high-flux ranging. IEEE Trans. Signal Process. 67, 3471–3486. https://doi.org/10.1109/TSP.2019.2914891 (2019).
doi: 10.1109/TSP.2019.2914891
Rapp, J., Ma, Y., Dawson, R. M. A. & Goyal, V. K. High-flux single-photon lidar. Optica 8, 30–39. https://doi.org/10.1364/OPTICA.403190 (2021).
doi: 10.1364/OPTICA.403190
Marais, W. J. & Hayman, M. Extending water vapor measurement capability of photon limited differential absorption lidars through simultaneous denoising and inversion. Atmos. Meas. Tech. 15, 5159–5180. https://doi.org/10.5194/amt-15-5159-2022 (2022).
doi: 10.5194/amt-15-5159-2022
Hayman, M., Stillwell, R. A., Karboski, A., Marais, W. J. & Spuler, S. M. Global estimation of range resolved thermodynamic profiles from micropulse differential absorption lidar. Opt. Express 32, 14442–14460. https://doi.org/10.1364/OE.521178 (2024).
doi: 10.1364/OE.521178
Becker, W. Advanced Time-Correlated Single Photon Counting Techniques 1st edn. (Springer, 2005).
O’Connor, D. V. & Phillips, D. Time-correlated single photon counting 1st edn. (Academic press, 1984).
Massa, J. et al. Time-of-flight optical ranging system based on time-correlated single photon counting. Appl. Opt. 37, 7298–7304. https://doi.org/10.1364/AO.37.007298 (1998).
doi: 10.1364/AO.37.007298
pubmed: 18301562
McCarthy, A. et al. Kilometer-range, high resolution depth imaging via 1560 nm wavelength single-photon detection. Opt. Express 21, 8904–8915. https://doi.org/10.1364/OE.21.008904 (2013).
doi: 10.1364/OE.21.008904
pubmed: 23571981
Pawlikowska, A. M., Halimi, A., Lamb, R. A. & Buller, G. S. Single-photon three-dimensional imaging at up to 10 kilometers range. Opt. Express 25, 11919–11931. https://doi.org/10.1364/OE.25.011919 (2017).
doi: 10.1364/OE.25.011919
pubmed: 28788749
Barton-Grimley, R. A., Stillwell, R. A. & Thayer, J. P. High resolution photon time-tagging lidar for atmospheric point cloud generation. Opt. Express 26, 26030–26044. https://doi.org/10.1364/OE.26.026030 (2018).
doi: 10.1364/OE.26.026030
pubmed: 30469696
Yang, F. et al. A time-gated, time-correlated single-photon-counting lidar to observe atmospheric clouds at submeter resolution. Remote Sens. 15, 1500. https://doi.org/10.3390/rs15061500 (2023).
doi: 10.3390/rs15061500
Alkasem, A. et al. Effects of cirrus heterogeneity on lidar caliop/calipso data. J. Quant. Spectrosc. Radiat. Transf. 202, 38–49. https://doi.org/10.1016/j.jqsrt.2017.07.005 (2017).
doi: 10.1016/j.jqsrt.2017.07.005
Arola, A. et al. Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors. Nat. Commun. 13, 7357. https://doi.org/10.1038/s41467-022-34948-5 (2022).
doi: 10.1038/s41467-022-34948-5
pubmed: 36446763
pmcid: 9708656
Shahverdi, A. et al. Mode selective up-conversion detection for lidar applications. Opt. Express 26, 15914–15923. https://doi.org/10.1364/OE.26.015914 (2018).
doi: 10.1364/OE.26.015914
pubmed: 30114845
Zhu, S. et al. Quantum parametric mode sorting: A case study on small angle scattering. JOSA B 38, D15–D21. https://doi.org/10.1364/JOSAB.430550 (2021).
doi: 10.1364/JOSAB.430550
Lee, J. et al. Quantum parametric mode sorting lidar for measurement of snow properties (2022). AGU Fall Meeting.
Timmermann, K. E. & Nowak, R. D. Multiscale modeling and estimation of Poisson processes with application to photon-limited imaging. IEEE Trans. Inf. Theory 45, 846–862. https://doi.org/10.1109/18.761328 (1999).
doi: 10.1109/18.761328
Harmany, Z. T., Marcia, R. F. & Willett, R. M. This is spiral-tap: Sparse Poisson intensity reconstruction algorithms-theory and practice. IEEE Trans. Image Process. 21, 1084–1096. https://doi.org/10.1109/TIP.2011.2168410 (2012).
doi: 10.1109/TIP.2011.2168410
pubmed: 21926022
Oh, A. K., Harmany, Z. T. & Willett, R. M. Logarithmic total variation regularization for cross-validation in photon-limited imaging. In 2013 IEEE International Conference on Image Processing, 484–488 (2013).
Heffes, H. & Lucantoni, D. M. A Markov modulated characterization of packetized voice and data traffic and related statistical multiplexer performance. IEEE J. Select. Areas Commun. 4, 856–868. https://doi.org/10.1109/JSAC.1986.1146393 (1986).
doi: 10.1109/JSAC.1986.1146393
Snyder, D. L., Hammoud, A. M. & White, R. L. Image recovery from data acquired with a charge-coupled-device camera. JOSA A 10, 1014–1023. https://doi.org/10.1364/JOSAA.10.001014 (1993).
doi: 10.1364/JOSAA.10.001014
Umasuthan, M., Wallace, A., Massa, J., Buller, G. & Walker, A. Processing time-correlated single photon counting data to acquire range images. IEEE Proc. Vis. Image Signal Process 145, 237–243. https://doi.org/10.1049/ip-vis:19982152 (1998).
doi: 10.1049/ip-vis:19982152
Altmann, Y., Ren, X., McCarthy, A., Buller, G. S. & McLaughlin, S. Robust bayesian target detection algorithm for depth imaging from sparse single-photon data. IEEE Trans. Comput. Imaging 2, 456–467. https://doi.org/10.1109/TCI.2016.2618323 (2016).
doi: 10.1109/TCI.2016.2618323
Halimi, A., Maccarone, A., McCarthy, A., McLaughlin, S. & Buller, G. S. Object depth profile and reflectivity restoration from sparse single-photon data acquired in underwater environments. IEEE Trans. Comput. Imaging 3, 472–484. https://doi.org/10.1109/TCI.2017.2669867 (2017).
doi: 10.1109/TCI.2017.2669867
Marais, W. J. et al. Approach to simultaneously denoise and invert backscatter and extinction from photon-limited atmospheric lidar observations. Appl. Opt. 55, 8316–8334. https://doi.org/10.1364/AO.55.008316 (2016).
doi: 10.1364/AO.55.008316
pubmed: 27828081
Snyder, D. L. & Miller, M. I. Random point processes in time and space 2nd edn. (Springer-Verlag, 1991).
Beck, A. & Teboulle, M. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18, 2419–2434. https://doi.org/10.1109/TIP.2009.2028250 (2009).
doi: 10.1109/TIP.2009.2028250
pubmed: 19635705
Hayman, M., Stillwell, R. A. & Spuler, S. M. Optimization of linear signal processing in photon counting lidar using Poisson thinning. Opt. Lett. 45, 5213–5216. https://doi.org/10.1364/OL.396498 (2020).
doi: 10.1364/OL.396498
pubmed: 32932493
Spuler, S. M. et al. Micropulse dial (mpd)—a diode-laser-based lidar architecture for quantitative atmospheric profiling. Atmos. Meas. Tech. 14, 4593–4616. https://doi.org/10.5194/amt-14-4593-2021 (2021).
doi: 10.5194/amt-14-4593-2021
NCAR/EOL Remote Sensing Facility. NCAR MPD data. UCAR/NCAR—Earth Observing Laboratory. https://doi.org/10.26023/MX0D-Z722-M406 . Accessed 2022.
Computational and Information Systems Laboratory, CISL. Cheyenne: HPE/SGI ICE XA System (NCAR Community Computing). Tech. Rep., National Center for Atmospheric Research (2020).
Hayman, Matthew, Stillwell, Robert A, Carnes, Josh, & Spuler, Scott M. Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data. UCAR/NCAR—Earth Observing Laboratory, https://doi.org/10.5281/zenodo.8341854 . Accessed 2023.