Multiple CR Spatiotemporal Compressive Imaging System

Higher spatial and temporal resolutions are two important performance parameters in an imaging system. However, due to hardware limitations, the two resolutions are usually mutually restricted. To meet this challenge, we propose a spatiotemporal compressive imaging (STCI) system to reconstruct high-spatiotemporal-resolution images from low-resolution measurements. For STCI, we also designed a novel reconstruction network for multiple compression ratio (CR). To verify the effectiveness of our method, we implemented simulation and optical experiments, respectively. The experiment results show that our method can effectively reconstruct high-spatiotemporal-resolution target scenes for nine different CRs. With the maximum spatiotemporal CR of 128:1, our method can achieve a reconstruction accuracy of 28.28 dB.

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Publication Details

Journal
Sensors
Published
2025-02-21
DOI
https://doi.org/10.3390/s25051334
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Multiple CR Spatiotemporal Compressive Imaging System

Dingaoyu Zhao, Jun Ke, Xiaowen Hao
Sensors
Sparse and Compressive Sensing Techniques
article

Multiple CR Spatiotemporal Compressive Imaging System

Dingaoyu Zhao, Jun Ke, Xiaowen Hao
article en

Abstract

Higher spatial and temporal resolutions are two important performance parameters in an imaging system. However, due to hardware limitations, the two resolutions are usually mutually restricted. To meet this challenge, we propose a spatiotemporal compressive imaging (STCI) system to reconstruct high-spatiotemporal-resolution images from low-resolution measurements. For STCI, we also designed a novel reconstruction network for multiple compression ratio (CR). To verify the effectiveness of our method, we implemented simulation and optical experiments, respectively. The experiment results show that our method can effectively reconstruct high-spatiotemporal-resolution target scenes for nine different CRs. With the maximum spatiotemporal CR of 128:1, our method can achieve a reconstruction accuracy of 28.28 dB.

SensorsVol. 25(5)
Beijing Institute of Technology (CN), Ministry of Education of the People's Republic of China (CN), Science and Technology on Surface Physics and Chemistry Laboratory (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 96%
Sparse and Compressive Sensing Techniques
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