Low‐light image enhancement for infrared and visible image fusion
Abstract Infrared and visible image fusion (IVIF) is an essential branch of image fusion, and enhancing the visible image of IVIF can significantly improve the fusion performance. However, many existing low‐light enhancement methods are unsuitable for the visible image enhancement of IVIF. In order...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
|
| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12857 |
| _version_ | 1797693072927096832 |
|---|---|
| author | Yiqiao Zhou Lisiqi Xie Kangjian He Dan Xu Dapeng Tao Xu Lin |
| author_facet | Yiqiao Zhou Lisiqi Xie Kangjian He Dan Xu Dapeng Tao Xu Lin |
| author_sort | Yiqiao Zhou |
| collection | DOAJ |
| description | Abstract Infrared and visible image fusion (IVIF) is an essential branch of image fusion, and enhancing the visible image of IVIF can significantly improve the fusion performance. However, many existing low‐light enhancement methods are unsuitable for the visible image enhancement of IVIF. In order to solve this problem, this paper proposes a new visible image enhancement method for IVIF. Firstly, the colour balance and contrast enhancement‐based self‐calibrated illumination estimation (CCSCE) is proposed to improve the input image's brightness, contrast, and colour information. Then, the method based on Mutually Guided Image Filtering (muGIF) is adopted to design a strategy to extract details adaptively from the original visible image, which can keep details without introducing additional noise effectively. Finally, the proposed visible image enhancement technique is used for IVIF tasks. In addition, the proposed method can be used for the visible image enhancement of IVIF and other low‐light images. Experiment results on different public datasets and IVIF demonstrate the authors’ method's superiority from both qualitative and quantitative comparisons. The authors’ code will be publicly available at https://github.com/yiqiao666/low‐light‐enhancement‐for‐IVIF/tree/master. |
| first_indexed | 2024-03-12T02:38:19Z |
| format | Article |
| id | doaj.art-68c116edd261400296eb720e20de246e |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-03-12T02:38:19Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-68c116edd261400296eb720e20de246e2023-09-04T10:54:49ZengWileyIET Image Processing1751-96591751-96672023-09-0117113216323410.1049/ipr2.12857Low‐light image enhancement for infrared and visible image fusionYiqiao Zhou0Lisiqi Xie1Kangjian He2Dan Xu3Dapeng Tao4Xu Lin5School of Information Science and Engineering Yunnan University KunmingP. R. ChinaSchool of Information Science and Engineering Yunnan University KunmingP. R. ChinaSchool of Information Science and Engineering Yunnan University KunmingP. R. ChinaSchool of Information Science and Engineering Yunnan University KunmingP. R. ChinaSchool of Information Science and Engineering Yunnan University KunmingP. R. ChinaYunnan Union Vision Innovation Technology Co Ltd KunmingP. R. ChinaAbstract Infrared and visible image fusion (IVIF) is an essential branch of image fusion, and enhancing the visible image of IVIF can significantly improve the fusion performance. However, many existing low‐light enhancement methods are unsuitable for the visible image enhancement of IVIF. In order to solve this problem, this paper proposes a new visible image enhancement method for IVIF. Firstly, the colour balance and contrast enhancement‐based self‐calibrated illumination estimation (CCSCE) is proposed to improve the input image's brightness, contrast, and colour information. Then, the method based on Mutually Guided Image Filtering (muGIF) is adopted to design a strategy to extract details adaptively from the original visible image, which can keep details without introducing additional noise effectively. Finally, the proposed visible image enhancement technique is used for IVIF tasks. In addition, the proposed method can be used for the visible image enhancement of IVIF and other low‐light images. Experiment results on different public datasets and IVIF demonstrate the authors’ method's superiority from both qualitative and quantitative comparisons. The authors’ code will be publicly available at https://github.com/yiqiao666/low‐light‐enhancement‐for‐IVIF/tree/master.https://doi.org/10.1049/ipr2.12857image denoisingimage enhancementimage fusion |
| spellingShingle | Yiqiao Zhou Lisiqi Xie Kangjian He Dan Xu Dapeng Tao Xu Lin Low‐light image enhancement for infrared and visible image fusion IET Image Processing image denoising image enhancement image fusion |
| title | Low‐light image enhancement for infrared and visible image fusion |
| title_full | Low‐light image enhancement for infrared and visible image fusion |
| title_fullStr | Low‐light image enhancement for infrared and visible image fusion |
| title_full_unstemmed | Low‐light image enhancement for infrared and visible image fusion |
| title_short | Low‐light image enhancement for infrared and visible image fusion |
| title_sort | low light image enhancement for infrared and visible image fusion |
| topic | image denoising image enhancement image fusion |
| url | https://doi.org/10.1049/ipr2.12857 |
| work_keys_str_mv | AT yiqiaozhou lowlightimageenhancementforinfraredandvisibleimagefusion AT lisiqixie lowlightimageenhancementforinfraredandvisibleimagefusion AT kangjianhe lowlightimageenhancementforinfraredandvisibleimagefusion AT danxu lowlightimageenhancementforinfraredandvisibleimagefusion AT dapengtao lowlightimageenhancementforinfraredandvisibleimagefusion AT xulin lowlightimageenhancementforinfraredandvisibleimagefusion |