Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network
Wide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current im...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/8/8/321 |
| _version_ | 1797522383045656576 |
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| author | Bowen Wang Yan Zou Linfei Zhang Yan Hu Hao Yan Chao Zuo Qian Chen |
| author_facet | Bowen Wang Yan Zou Linfei Zhang Yan Hu Hao Yan Chao Zuo Qian Chen |
| author_sort | Bowen Wang |
| collection | DOAJ |
| description | Wide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current imaging sensors to obtain high spatial resolution is limited, especially under low-light-level (LLL) imaging conditions. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize pixel-super-resolved LLL imaging. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, the calculation of the high-frequency components can receive greater attention. Compared with other networks, the peak signal-to-noise ratio of the reconstructed image was increased by 1.67 dB. Extensions of the MSFE network are investigated for scene-based color mapping of the gray image. Most of the color information could be recovered, and the similarity with the real image reached 0.728. The qualitative and quantitative experimental results show that the proposed method achieved superior performance in image fidelity and detail enhancement over the state-of-the-art. |
| first_indexed | 2024-03-10T08:28:40Z |
| format | Article |
| id | doaj.art-be1e7d7a3b994e208ecf2693816add8a |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-10T08:28:40Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-be1e7d7a3b994e208ecf2693816add8a2023-11-22T09:16:36ZengMDPI AGPhotonics2304-67322021-08-018832110.3390/photonics8080321Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction NetworkBowen Wang0Yan Zou1Linfei Zhang2Yan Hu3Hao Yan4Chao Zuo5Qian Chen6Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaMilitary Representative Office of Army Equipment Department in Taian, Taian 271000, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaJiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, ChinaWide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current imaging sensors to obtain high spatial resolution is limited, especially under low-light-level (LLL) imaging conditions. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize pixel-super-resolved LLL imaging. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, the calculation of the high-frequency components can receive greater attention. Compared with other networks, the peak signal-to-noise ratio of the reconstructed image was increased by 1.67 dB. Extensions of the MSFE network are investigated for scene-based color mapping of the gray image. Most of the color information could be recovered, and the similarity with the real image reached 0.728. The qualitative and quantitative experimental results show that the proposed method achieved superior performance in image fidelity and detail enhancement over the state-of-the-art.https://www.mdpi.com/2304-6732/8/8/321super resolutionlow-light-leveldeep learning networkmulti-scale feature extraction |
| spellingShingle | Bowen Wang Yan Zou Linfei Zhang Yan Hu Hao Yan Chao Zuo Qian Chen Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network Photonics super resolution low-light-level deep learning network multi-scale feature extraction |
| title | Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network |
| title_full | Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network |
| title_fullStr | Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network |
| title_full_unstemmed | Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network |
| title_short | Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network |
| title_sort | low light level image super resolution reconstruction based on a multi scale features extraction network |
| topic | super resolution low-light-level deep learning network multi-scale feature extraction |
| url | https://www.mdpi.com/2304-6732/8/8/321 |
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