Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask
Image fusion integrates complex information about a target scene from multiple sensors into a single image. The fused image can further be utilized for human perception or different machine vision tasks. In the case of infrared and visible images, infrared images have the advantage of capturing ther...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/8894856/ |
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| author | Lei Yan Jie Cao Yang Cheng Saad Rizvi Qun Hao |
| author_facet | Lei Yan Jie Cao Yang Cheng Saad Rizvi Qun Hao |
| author_sort | Lei Yan |
| collection | DOAJ |
| description | Image fusion integrates complex information about a target scene from multiple sensors into a single image. The fused image can further be utilized for human perception or different machine vision tasks. In the case of infrared and visible images, infrared images have the advantage of capturing thermal radiation intensity, whereas visible images are superior in gradient texture. In order to effectively fuse thermal intensity of infrared image and texture advantage of visible image, we propose a novel fusion method based on L0 decomposition and intensity mask. The proposed method first acquires base and detail layers of images (visible & infrared) using L0 decomposition. Next, an intensity mask is obtained using the basic global thresholding method on base layers of infrared image. The layers (base layers and detail layers) and visible images are divided images into three parts by the use of intensity mask, namely, mask-base layers, mask-detail layers, and texture-background. The first and second parts effectively achieve intensity blending, whereas the third part achieves the fused image with a clear gradient texture. The proposed method shows superior performance when compared with five state-of-the-art methods (on publicly available databases). |
| format | Article |
| id | doaj-art-7ef252637ea34ddcb083c9be66dcbb06 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-7ef252637ea34ddcb083c9be66dcbb062025-08-20T03:32:54ZengIEEEIEEE Photonics Journal1943-06552019-01-0111611110.1109/JPHOT.2019.29526548894856Infrared and Visible Image Fusion via L0 Decomposition and Intensity MaskLei Yan0https://orcid.org/0000-0002-7152-0519Jie Cao1https://orcid.org/0000-0001-8376-7669Yang Cheng2Saad Rizvi3https://orcid.org/0000-0003-3942-0857Qun Hao4https://orcid.org/0000-0003-2577-9391Key Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing, ChinaKey Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing, ChinaImage fusion integrates complex information about a target scene from multiple sensors into a single image. The fused image can further be utilized for human perception or different machine vision tasks. In the case of infrared and visible images, infrared images have the advantage of capturing thermal radiation intensity, whereas visible images are superior in gradient texture. In order to effectively fuse thermal intensity of infrared image and texture advantage of visible image, we propose a novel fusion method based on L0 decomposition and intensity mask. The proposed method first acquires base and detail layers of images (visible & infrared) using L0 decomposition. Next, an intensity mask is obtained using the basic global thresholding method on base layers of infrared image. The layers (base layers and detail layers) and visible images are divided images into three parts by the use of intensity mask, namely, mask-base layers, mask-detail layers, and texture-background. The first and second parts effectively achieve intensity blending, whereas the third part achieves the fused image with a clear gradient texture. The proposed method shows superior performance when compared with five state-of-the-art methods (on publicly available databases).https://ieeexplore.ieee.org/document/8894856/Image fusionL0 decompositionintensity maskmaximum gradient. |
| spellingShingle | Lei Yan Jie Cao Yang Cheng Saad Rizvi Qun Hao Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask IEEE Photonics Journal Image fusion L0 decomposition intensity mask maximum gradient. |
| title | Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask |
| title_full | Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask |
| title_fullStr | Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask |
| title_full_unstemmed | Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask |
| title_short | Infrared and Visible Image Fusion via L0 Decomposition and Intensity Mask |
| title_sort | infrared and visible image fusion via l0 decomposition and intensity mask |
| topic | Image fusion L0 decomposition intensity mask maximum gradient. |
| url | https://ieeexplore.ieee.org/document/8894856/ |
| work_keys_str_mv | AT leiyan infraredandvisibleimagefusionvial0decompositionandintensitymask AT jiecao infraredandvisibleimagefusionvial0decompositionandintensitymask AT yangcheng infraredandvisibleimagefusionvial0decompositionandintensitymask AT saadrizvi infraredandvisibleimagefusionvial0decompositionandintensitymask AT qunhao infraredandvisibleimagefusionvial0decompositionandintensitymask |