Dual Optical Path Based Adaptive Compressive Sensing Imaging System
Compressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the lig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6200 |
| _version_ | 1827681225391734784 |
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| author | Hongliang Li Ke Lu Jian Xue Feng Dai Yongdong Zhang |
| author_facet | Hongliang Li Ke Lu Jian Xue Feng Dai Yongdong Zhang |
| author_sort | Hongliang Li |
| collection | DOAJ |
| description | Compressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the light path and the circuit, the measurements finally obtained are noisy, which directly affects the imaging quality. We propose a dual-optical imaging system that uses the bidirectional reflection characteristics of digital micromirror devices (DMD) to simultaneously acquire CS measurements and images under the same viewing angle. Since deep neural networks have powerful modeling capabilities, we trained the filter network and the reconstruction network separately. The filter network is used to filter the noise in the measurements, and the reconstruction network is used to reconstruct the CS image. Experiments have proved that the method we proposed can filter the noise in the sampling process of the CS system, and can significantly improve the quality of image reconstruction under a variety of algorithms. |
| first_indexed | 2024-03-10T07:13:12Z |
| format | Article |
| id | doaj.art-186f373bfeb749cd87dbc595c034bceb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:12Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-186f373bfeb749cd87dbc595c034bceb2023-11-22T15:13:12ZengMDPI AGSensors1424-82202021-09-012118620010.3390/s21186200Dual Optical Path Based Adaptive Compressive Sensing Imaging SystemHongliang Li0Ke Lu1Jian Xue2Feng Dai3Yongdong Zhang4School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, ChinaInstitute of Computing Technology, CAS, Beijing 100190, ChinaSchool of Information Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaCompressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the light path and the circuit, the measurements finally obtained are noisy, which directly affects the imaging quality. We propose a dual-optical imaging system that uses the bidirectional reflection characteristics of digital micromirror devices (DMD) to simultaneously acquire CS measurements and images under the same viewing angle. Since deep neural networks have powerful modeling capabilities, we trained the filter network and the reconstruction network separately. The filter network is used to filter the noise in the measurements, and the reconstruction network is used to reconstruct the CS image. Experiments have proved that the method we proposed can filter the noise in the sampling process of the CS system, and can significantly improve the quality of image reconstruction under a variety of algorithms.https://www.mdpi.com/1424-8220/21/18/6200compressive sensingimaging systemneural networks |
| spellingShingle | Hongliang Li Ke Lu Jian Xue Feng Dai Yongdong Zhang Dual Optical Path Based Adaptive Compressive Sensing Imaging System Sensors compressive sensing imaging system neural networks |
| title | Dual Optical Path Based Adaptive Compressive Sensing Imaging System |
| title_full | Dual Optical Path Based Adaptive Compressive Sensing Imaging System |
| title_fullStr | Dual Optical Path Based Adaptive Compressive Sensing Imaging System |
| title_full_unstemmed | Dual Optical Path Based Adaptive Compressive Sensing Imaging System |
| title_short | Dual Optical Path Based Adaptive Compressive Sensing Imaging System |
| title_sort | dual optical path based adaptive compressive sensing imaging system |
| topic | compressive sensing imaging system neural networks |
| url | https://www.mdpi.com/1424-8220/21/18/6200 |
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