An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information Article Swipe
Qingqing Li
,
Guangliang Han
,
Peixun Liu
,
Hang Yang
,
Jiajia Wu
,
Dongxu Liu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3101639
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3101639
Infrared and visible image fusion is a hot topic due to the perfect complementarity of their information. There are two key problems in infrared and visible image fusion. One is how to extract significant target areas and rich texture details from the source images, and the other is how to integrate them to produce satisfactory fused images. To tackle these problems, we propose a novel fusion framework in this paper. A multi-level image decomposition method is used to obtain the base layer and detail layer of the source image. For the fusion of base layer, an ingenious fusion strategy guided by the saliency map of source image is designed to improve the intensity of salient targets and the visual quality of the fused image. For the fusion of detail layer, an efficient approach by introducing the enhanced gradient information is presented to boost the detail features and sharpen the edges of the fused image. Experimental results demonstrate that, compared with fifteen classical and advanced fusion methods, the proposed image fusion framework has better performance in both subjective and objective evaluation.
Related Topics
Concepts
Image fusion
Fusion
Artificial intelligence
Computer science
Computer vision
Salient
Image (mathematics)
Complementarity (molecular biology)
Image texture
Image gradient
Layer (electronics)
Pattern recognition (psychology)
Image processing
Materials science
Composite material
Biology
Genetics
Linguistics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3101639
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09502600.pdf
- OA Status
- gold
- Cited By
- 14
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3192841331
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3192841331Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3101639Digital Object Identifier
- Title
-
An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient InformationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Qingqing Li, Guangliang Han, Peixun Liu, Hang Yang, Jiajia Wu, Dongxu LiuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3101639Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09502600.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09502600.pdfDirect OA link when available
- Concepts
-
Image fusion, Fusion, Artificial intelligence, Computer science, Computer vision, Salient, Image (mathematics), Complementarity (molecular biology), Image texture, Image gradient, Layer (electronics), Pattern recognition (psychology), Image processing, Materials science, Composite material, Biology, Genetics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 5, 2023: 2, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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