A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images
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...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-01-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0231903 |
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| author | Yuchao Wang Xuyang Wei Chunyang Wang Xuelian Liu Da Xie Kai Yuan Rong Li |
| author_facet | Yuchao Wang Xuyang Wei Chunyang Wang Xuelian Liu Da Xie Kai Yuan Rong Li |
| author_sort | Yuchao Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-794cfbed20314952bf421b886da2187f |
| institution | Kabale University |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIP Publishing LLC |
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| series | AIP Advances |
| spelling | doaj-art-794cfbed20314952bf421b886da2187f2025-02-03T16:40:42ZengAIP Publishing LLCAIP Advances2158-32262025-01-01151015102015102-1210.1063/5.0231903A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR imagesYuchao Wang0Xuyang Wei1Chunyang Wang2Xuelian Liu3Da Xie4Kai Yuan5Rong Li6School of Opto-electronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Opto-electronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Opto-electronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Opto-electronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Opto-electronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaTo 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.http://dx.doi.org/10.1063/5.0231903 |
| spellingShingle | Yuchao Wang Xuyang Wei Chunyang Wang Xuelian Liu Da Xie Kai Yuan Rong Li A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images AIP Advances |
| title | A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images |
| title_full | A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images |
| title_fullStr | A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images |
| title_full_unstemmed | A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images |
| title_short | A non-local regularization-based fractional-order total variational compressive sensing algorithm for effective recovery of Geiger-mode avalanche photodiode LiDAR images |
| title_sort | non local regularization based fractional order total variational compressive sensing algorithm for effective recovery of geiger mode avalanche photodiode lidar images |
| url | http://dx.doi.org/10.1063/5.0231903 |
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