AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion Article Swipe
Yufeng Hu
,
Shaoping Xu
,
Wei-Hua Lin
,
Changfei Zhou
,
Minghai Xiong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13095640
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13095640
To obtain fused images with excellent contrast, distinct target edges, and well-preserved details, we propose an adaptive image fusion network called the adjacent feature shuffle-fusion network (AFSFusion). The proposed network adopts a UNet-like architecture and incorporates key refinements to enhance network architecture and loss functions. Regarding the network architecture, the proposed two-branch adjacent feature fusion module, called AFSF, expands the number of channels to fuse the feature channels of several adjacent convolutional layers in the first half of the AFSFusion, enhancing its ability to extract, transmit, and modulate feature information. We replace the original rectified linear unit (ReLU) with leaky ReLU to alleviate the problem of gradient disappearance and add a channel shuffling operation at the end of AFSF to facilitate information interaction capability between features. Concerning loss functions, we propose an adaptive weight adjustment (AWA) strategy to assign weight values to the corresponding pixels of the infrared (IR) and visible images in the fused images, according to the VGG16 gradient feature response of the IR and visible images. This strategy efficiently handles different scene contents. After normalization, the weight values are used as weighting coefficients for the two sets of images. The weighting coefficients are applied to three loss items simultaneously: mean square error (MSE), structural similarity (SSIM), and total variation (TV), resulting in clearer objects and richer texture detail in the fused images. We conducted a series of experiments on several benchmark databases, and the results demonstrate the effectiveness of the proposed network architecture and the superiority of the proposed network compared to other state-of-the-art fusion methods. It ranks first in several objective metrics, showing the best performance and exhibiting sharper and richer edges of specific targets, which is more in line with human visual perception. The remarkable enhancement in performance is ascribed to the proposed AFSF module and AWA strategy, enabling balanced feature extraction, fusion, and modulation of image features throughout the process.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13095640
- https://www.mdpi.com/2076-3417/13/9/5640/pdf?version=1683277123
- OA Status
- gold
- Cited By
- 2
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4368366478
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4368366478Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app13095640Digital Object Identifier
- Title
-
AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image FusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-03Full publication date if available
- Authors
-
Yufeng Hu, Shaoping Xu, Wei-Hua Lin, Changfei Zhou, Minghai XiongList of authors in order
- Landing page
-
https://doi.org/10.3390/app13095640Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/9/5640/pdf?version=1683277123Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/13/9/5640/pdf?version=1683277123Direct OA link when available
- Concepts
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Weighting, Feature (linguistics), Computer science, Artificial intelligence, Pattern recognition (psychology), Pixel, Normalization (sociology), Benchmark (surveying), Network architecture, Computer vision, Geography, Philosophy, Linguistics, Geodesy, Computer security, Anthropology, Medicine, Radiology, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-05-03 |
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