A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images
Source abstract
Abstract
To address the challenge of low accuracy of the range image recovery of Geiger-mode avalanche photodiode (GM-APD) LiDAR in low signal-to-background ratios (SBRs), this paper proposes a non-local regularization-based fractional-order total variational compressive sensing (CS) algorithm for recovering GM-APD LiDAR images. First, the kurtosis factor peak method was utilized to obtain the target range image at low SBRs. Subsequently, a non-local regularization-based fractional-order total variational CS model for GM-APD LiDAR image recovery is proposed, leveraging the sparse and constrained isometric properties of CS as well as the memorability of fractional-order calculus. This model aims to compress and sample high-dimensional range images while performing a sparse representation. Finally, an augmented Lagrange algorithm was employed to precisely recover the target range image. The results of the experiments demonstrate that the proposed method can enhance the degree of target recovery by a minimum of 4.29% and increase the peak signal-to-noise ratio by at least 9.29% under conditions of a 60% sampling rate, identical SBR, and statistical frame number.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sparse and Compressive Sensing Techniques · Advanced Optical Sensing Technologies · Photoacoustic and Ultrasonic Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Opto-electronic Engineering, Xi’an Technological University 1 , Xi’an 710021,
School of Electronic and Information Engineering, Changchun University of Science and Technology 2 , Changchun 130022,
Funding
National Key Research and Development Program of China · National Innovation Talent Promotion Program
References (36)
Show source-provided references
1. Geiger-mode avalanche photodiode ladar receiver performance characteristics and detection statistics10.1364/ao.48.003261 ↗
2. 3D reconstruction of the dynamic scene with high-speed targets for GM-APD LiDAR10.1016/j.optlastec.2023.109114 ↗
3. Research on infrared image guided GM-APD range image recovery algorithm under limited detections10.1016/j.optlaseng.2023.107579 ↗
4. Suppressing the influence of GM-APD coherent lidar saturation by signal modulation10.1016/j.ijleo.2023.170619 ↗
5. Long-distance multi-vehicle detection at night based on Gm-APD lidar10.3390/rs14153553 ↗
6. Performance analysis of the photon-counting LIDAR based on the statistical property10.1007/978-981-19-3387-5 ↗
7. Single-parameter estimation construction algorithm for Gm-APD ladar imaging through fog10.1016/j.optcom.2020.126558 ↗
8. Dual-parameter estimation algorithm for Gm-APD Lidar depth imaging through smoke10.1016/j.measurement.2022.111269 ↗
9. Adaptive suppression method of LiDAR background noise based on threshold detection10.3390/app13063772 ↗
10. Prediction of the number of cumulative pulses based on the photon statistical entropy evaluation in photon-counting LiDAR10.3390/e25030522 ↗
11. Restoration of intensity and depth images constructed using sparse single-photon data
12. Photon-efficient computational 3-D and reflectivity imaging with single-photon detectors10.1109/tci.2015.2453093 ↗
13. Single-photon depth imaging using a union-of-subspaces model10.1109/lsp.2015.2475274 ↗
14. A few photons among many: Unmixing signal and noise for photon-efficient active imaging10.1109/tci.2017.2706028 ↗
15. Learning non-local spatial correlations to restore sparse 3D single-photon data10.1109/tip.2019.2957918 ↗
16. First-photon imaging via a hybrid penalty10.1364/prj.381516 ↗
17. Efficient and noise robust photon-counting imaging with first signal photon unit method10.3390/photonics8060229 ↗
18. A photon-number-based systematic algorithm for range image recovery of GM-APD lidar under few-frames detection10.1016/j.infrared.2022.104267 ↗
19. General image denoising framework based on compressive sensing theory10.1016/j.cag.2013.11.011 ↗
20. Enhanced 3DTV regularization and its applications on HSI denoising and compressed sensing10.1109/tip.2020.3007840 ↗
21. A fractional-order total variation regularization-based method for recovering Geiger-mode avalanche photodiode light detection and ranging depth images10.3390/fractalfract7060445 ↗
22. A spatially correlated fractional integral-based method for denoising Geiger-mode avalanche photodiode light detection and ranging depth images10.1016/j.ijleo.2023.171244 ↗
23. Applications of fractional operator in image processing and stability of control systems10.3390/fractalfract7050359 ↗
24. A unified framework of stability theorems for LTI fractional order systems with 0 < α < 210.1109/tcsii.2020.2978869 ↗
25. Improved total variation based image compressive sensing recovery by nonlocal regularization
26. Time-correlated Kalman depth estimation of photon-counting Lidar10.3788/gzxb20215003.0311001 ↗
27. Image denoising using a compressive sensing approach based on regularization constraints10.3390/s22062199 ↗
28. Compressive sensing image reconstruction via recursive spatially adaptive filtering
29. Block compressive sensing of image and video with nonlocal Lagrangian multiplier and patch-based sparse representation10.1016/j.image.2017.02.012 ↗
30. Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces10.1006/mssp.1997.0115 ↗
31. Total variation regularization sparse-view reconstruction algorithm combined with neighborhood information
32. Accelerated fast BOTDA assisted by compressed sensing and image denoising10.1109/jsen.2021.3117287 ↗
33. Image denoising by multiple compressed sensing reconstructions
34. Research on 3D range reconstruction algorithm of Gm-APD lidar based on matched filter
35. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work10.1016/j.artmed.2022.102314 ↗
36. Simulation of 3D laser imaging10.2529/piers091218131550 ↗