Method for GPU-based spectral data cube reconstruction of integral field snapshot imaging spectrometers
In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:32 |
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| Enthalten in: |
Optics express - 32(2024), 7 vom: 25. März, Seite 11123-11133 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ding, Hailei [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 04.04.2024 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1364/OE.516882 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM370603931 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Zhu, Yunxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Haokang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Jiacheng |e verfasserin |4 aut | |
| 700 | 1 | |a Zou, Yibo |e verfasserin |4 aut | |
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