Low Light Image Enhancement Network With Attention Mechanism and Retinex Model
Due to the uncertainty of the environment, the captured image may not only degrade but also have uneven brightness distribution. The quality of these images can not meet the input requirements of existing computer vision tasks, so they may lead to performance degradation in the completion of compute...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9072125/ |
| _version_ | 1819176151173562368 |
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| author | Wei Huang Yifeng Zhu Rui Huang |
| author_facet | Wei Huang Yifeng Zhu Rui Huang |
| author_sort | Wei Huang |
| collection | DOAJ |
| description | Due to the uncertainty of the environment, the captured image may not only degrade but also have uneven brightness distribution. The quality of these images can not meet the input requirements of existing computer vision tasks, so they may lead to performance degradation in the completion of computer vision tasks. Past methods are often only applicable to low-light images with uniform brightness distribution, and the performance of these methods is not ideal for low-light images with uneven brightness distribution. In order to solve the problems caused by these low-light images, a new low light image enhancement model based on attention mechanism and Retinex model is proposed. The proposed method first estimates the illumination mask of the input image, which guides the network to predict the illumination distribution. Then we use a module with attention mechanism to predict the illumination map, and the initial enhanced image is estimated based on Retinex model. We modify the color distortion and suppress noise with convolution layers to obtain final enhanced results. In experiments, the performance of our methods is demonstrated by compared with the state-of-the-art existing methods. Our approach has more positive performance in some scenarios, especially uneven lighting. |
| first_indexed | 2024-12-22T21:06:12Z |
| format | Article |
| id | doaj.art-0e4cba8e8c8f465f9c29cbf0bb8f2098 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:06:12Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0e4cba8e8c8f465f9c29cbf0bb8f20982022-12-21T18:12:39ZengIEEEIEEE Access2169-35362020-01-018743067431410.1109/ACCESS.2020.29887679072125Low Light Image Enhancement Network With Attention Mechanism and Retinex ModelWei Huang0Yifeng Zhu1https://orcid.org/0000-0002-2290-1411Rui Huang2School of Communication and Information Engineering, Shanghai University, Shanghai, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai, ChinaDue to the uncertainty of the environment, the captured image may not only degrade but also have uneven brightness distribution. The quality of these images can not meet the input requirements of existing computer vision tasks, so they may lead to performance degradation in the completion of computer vision tasks. Past methods are often only applicable to low-light images with uniform brightness distribution, and the performance of these methods is not ideal for low-light images with uneven brightness distribution. In order to solve the problems caused by these low-light images, a new low light image enhancement model based on attention mechanism and Retinex model is proposed. The proposed method first estimates the illumination mask of the input image, which guides the network to predict the illumination distribution. Then we use a module with attention mechanism to predict the illumination map, and the initial enhanced image is estimated based on Retinex model. We modify the color distortion and suppress noise with convolution layers to obtain final enhanced results. In experiments, the performance of our methods is demonstrated by compared with the state-of-the-art existing methods. Our approach has more positive performance in some scenarios, especially uneven lighting.https://ieeexplore.ieee.org/document/9072125/Attention mechanismimage enhancementillumination mapRetinex model |
| spellingShingle | Wei Huang Yifeng Zhu Rui Huang Low Light Image Enhancement Network With Attention Mechanism and Retinex Model IEEE Access Attention mechanism image enhancement illumination map Retinex model |
| title | Low Light Image Enhancement Network With Attention Mechanism and Retinex Model |
| title_full | Low Light Image Enhancement Network With Attention Mechanism and Retinex Model |
| title_fullStr | Low Light Image Enhancement Network With Attention Mechanism and Retinex Model |
| title_full_unstemmed | Low Light Image Enhancement Network With Attention Mechanism and Retinex Model |
| title_short | Low Light Image Enhancement Network With Attention Mechanism and Retinex Model |
| title_sort | low light image enhancement network with attention mechanism and retinex model |
| topic | Attention mechanism image enhancement illumination map Retinex model |
| url | https://ieeexplore.ieee.org/document/9072125/ |
| work_keys_str_mv | AT weihuang lowlightimageenhancementnetworkwithattentionmechanismandretinexmodel AT yifengzhu lowlightimageenhancementnetworkwithattentionmechanismandretinexmodel AT ruihuang lowlightimageenhancementnetworkwithattentionmechanismandretinexmodel |