Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning
Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/19/4190 |
| _version_ | 1798041252080386048 |
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| author | Saad Rizvi Jie Cao Kaiyu Zhang Qun Hao |
| author_facet | Saad Rizvi Jie Cao Kaiyu Zhang Qun Hao |
| author_sort | Saad Rizvi |
| collection | DOAJ |
| description | Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5−8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates. |
| first_indexed | 2024-04-11T22:18:53Z |
| format | Article |
| id | doaj.art-291ce1bddd6e48c6949540cec81879c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:18:53Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-291ce1bddd6e48c6949540cec81879c52022-12-22T04:00:15ZengMDPI AGSensors1424-82202019-09-011919419010.3390/s19194190s19194190Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep LearningSaad Rizvi0Jie Cao1Kaiyu Zhang2Qun Hao3Key Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaFourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5−8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.https://www.mdpi.com/1424-8220/19/19/4190computational imagingfourier single-pixel imagingdeep learning |
| spellingShingle | Saad Rizvi Jie Cao Kaiyu Zhang Qun Hao Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning Sensors computational imaging fourier single-pixel imaging deep learning |
| title | Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning |
| title_full | Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning |
| title_fullStr | Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning |
| title_full_unstemmed | Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning |
| title_short | Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning |
| title_sort | improving imaging quality of real time fourier single pixel imaging via deep learning |
| topic | computational imaging fourier single-pixel imaging deep learning |
| url | https://www.mdpi.com/1424-8220/19/19/4190 |
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