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|a 10.1109/TIP.2023.3322046
|2 doi
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|a DE-627
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|a eng
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|a Yue, Jun
|e verfasserin
|4 aut
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| 245 |
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|a Dif-Fusion
|b Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 25.10.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Color plays an important role in human visual perception, reflecting the spectrum of objects. However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high color fidelity. This paper addresses the above issue by proposing a novel method with diffusion models, termed as Dif-Fusion, to generate the distribution of the multi-channel input data, which increases the ability of multi-source information aggregation and the fidelity of colors. In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process. Then, we use the the denoising network to extract the multi-channel diffusion features with both visible and infrared information. Finally, we feed the multi-channel diffusion features to the multi-channel fusion module to directly generate the three-channel fused image. To retain the texture and intensity information, we propose multi-channel gradient loss and intensity loss. Along with the current evaluation metrics for measuring texture and intensity fidelity, we introduce Delta E as a new evaluation metric to quantify color fidelity. Extensive experiments indicate that our method is more effective than other state-of-the-art image fusion methods, especially in color fidelity. The source code is available at https://github.com/GeoVectorMatrix/Dif-Fusion
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|a Journal Article
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1 |
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|a Fang, Leyuan
|e verfasserin
|4 aut
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| 700 |
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|a Xia, Shaobo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Deng, Yue
|e verfasserin
|4 aut
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| 700 |
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|a Ma, Jiayi
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 01., Seite 5705-5720
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|x 1941-0042
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|g volume:32
|g year:2023
|g day:01
|g pages:5705-5720
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|u http://dx.doi.org/10.1109/TIP.2023.3322046
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