Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain
We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and ga...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Digital Multimedia Broadcasting |
| Online Access: | http://dx.doi.org/10.1155/2017/9029315 |
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| _version_ | 1832559392013156352 |
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| author | Xiankun Sun Huijie Liu Shiqian Wu Zhijun Fang Chengfan Li Jingyuan Yin |
| author_facet | Xiankun Sun Huijie Liu Shiqian Wu Zhijun Fang Chengfan Li Jingyuan Yin |
| author_sort | Xiankun Sun |
| collection | DOAJ |
| description | We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation. |
| format | Article |
| id | doaj-art-fe50baf330c14dad9126f8d4b339f8d2 |
| institution | Kabale University |
| issn | 1687-7578 1687-7586 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Digital Multimedia Broadcasting |
| spelling | doaj-art-fe50baf330c14dad9126f8d4b339f8d22025-02-03T01:30:12ZengWileyInternational Journal of Digital Multimedia Broadcasting1687-75781687-75862017-01-01201710.1155/2017/90293159029315Low-Light Image Enhancement Based on Guided Image Filtering in Gradient DomainXiankun Sun0Huijie Liu1Shiqian Wu2Zhijun Fang3Chengfan Li4Jingyuan Yin5School of Computer Engineering and Science, Shanghai University, Shanghai, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaWe propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation.http://dx.doi.org/10.1155/2017/9029315 |
| spellingShingle | Xiankun Sun Huijie Liu Shiqian Wu Zhijun Fang Chengfan Li Jingyuan Yin Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain International Journal of Digital Multimedia Broadcasting |
| title | Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain |
| title_full | Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain |
| title_fullStr | Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain |
| title_full_unstemmed | Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain |
| title_short | Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain |
| title_sort | low light image enhancement based on guided image filtering in gradient domain |
| url | http://dx.doi.org/10.1155/2017/9029315 |
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