Computational Depth Imaging Using the Fast Deconvolution Method
Pseudo-random spread spectrum photon counting (PSSPC) is a well-established technique for three-dimensional (3D) imaging. Based on the pseudo-random spread spectrum photon counting system, a fast imaging technique that is able to accurately recover multiple depths at individual pixels is presented....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8827505/ |
| _version_ | 1831539926472065024 |
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| author | Shen Shanshan Chen Qian He Wei Ji Gu Guo Hua |
| author_facet | Shen Shanshan Chen Qian He Wei Ji Gu Guo Hua |
| author_sort | Shen Shanshan |
| collection | DOAJ |
| description | Pseudo-random spread spectrum photon counting (PSSPC) is a well-established technique for three-dimensional (3D) imaging. Based on the pseudo-random spread spectrum photon counting system, a fast imaging technique that is able to accurately recover multiple depths at individual pixels is presented. Firstly, a pre-filtering algorithm is used to denoise the original data. Then the accelerated Richardson-Lucy iterative deconvolution algorithm is introduced. The method is based on the principles of vector extrapolation and does not require the minimization of a cost function. For multi-depth estimation in the presence of moderate background light, we experimentally demonstrate that our imaging technique outperforms the existing method. We have successfully improved the range resolution from 21cm to 8cm, thus breaking the Full Width at Half-Maximum (FWHM) resolution limit. The separation Root Mean Square Error (RMSE) has been reduced to 3.82cm by the proposed method for the surface-to-surface separation of 8cm. This is a factor of 4 improvement over the conventional method for multi-depth recovery. Also, our imager has achieved 0.5cm lateral resolution by distinguishing two squares closely placed 0.5cm apart from each other. |
| first_indexed | 2024-12-16T23:49:42Z |
| format | Article |
| id | doaj.art-bf9f0f0e03d6400da3818018cdec053c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:49:42Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf9f0f0e03d6400da3818018cdec053c2022-12-21T22:11:22ZengIEEEIEEE Access2169-35362019-01-01713215313216010.1109/ACCESS.2019.29397848827505Computational Depth Imaging Using the Fast Deconvolution MethodShen Shanshan0https://orcid.org/0000-0003-2754-0448Chen Qian1He Wei Ji2Gu Guo Hua3Jiangsu Key Laboratory of Spectral Imaging and Intelligence Sense (SIIS), Nanjing University of Science and Technology, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligence Sense (SIIS), Nanjing University of Science and Technology, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligence Sense (SIIS), Nanjing University of Science and Technology, Nanjing, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligence Sense (SIIS), Nanjing University of Science and Technology, Nanjing, ChinaPseudo-random spread spectrum photon counting (PSSPC) is a well-established technique for three-dimensional (3D) imaging. Based on the pseudo-random spread spectrum photon counting system, a fast imaging technique that is able to accurately recover multiple depths at individual pixels is presented. Firstly, a pre-filtering algorithm is used to denoise the original data. Then the accelerated Richardson-Lucy iterative deconvolution algorithm is introduced. The method is based on the principles of vector extrapolation and does not require the minimization of a cost function. For multi-depth estimation in the presence of moderate background light, we experimentally demonstrate that our imaging technique outperforms the existing method. We have successfully improved the range resolution from 21cm to 8cm, thus breaking the Full Width at Half-Maximum (FWHM) resolution limit. The separation Root Mean Square Error (RMSE) has been reduced to 3.82cm by the proposed method for the surface-to-surface separation of 8cm. This is a factor of 4 improvement over the conventional method for multi-depth recovery. Also, our imager has achieved 0.5cm lateral resolution by distinguishing two squares closely placed 0.5cm apart from each other.https://ieeexplore.ieee.org/document/8827505/Pseudo-random spread spectrum photon countingdeconvolutionaccelerated Richardson–Lucyseparation RMSE |
| spellingShingle | Shen Shanshan Chen Qian He Wei Ji Gu Guo Hua Computational Depth Imaging Using the Fast Deconvolution Method IEEE Access Pseudo-random spread spectrum photon counting deconvolution accelerated Richardson–Lucy separation RMSE |
| title | Computational Depth Imaging Using the Fast Deconvolution Method |
| title_full | Computational Depth Imaging Using the Fast Deconvolution Method |
| title_fullStr | Computational Depth Imaging Using the Fast Deconvolution Method |
| title_full_unstemmed | Computational Depth Imaging Using the Fast Deconvolution Method |
| title_short | Computational Depth Imaging Using the Fast Deconvolution Method |
| title_sort | computational depth imaging using the fast deconvolution method |
| topic | Pseudo-random spread spectrum photon counting deconvolution accelerated Richardson–Lucy separation RMSE |
| url | https://ieeexplore.ieee.org/document/8827505/ |
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