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A unified image fusion framework with flexible bilevel paradigm integration

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Abstract

Multi-modality image fusion refers to integrating series of images acquired from different sensors and obtaining a fused image which is expected to provide more comprehensive information. It plays a pivotal role in many computer vision tasks and promotes the performance of subsequent applications. Most existing approaches attempted to design appropriate fusion rules for specific image fusion task, which have a bad generalization to different fusion tasks. Apart from that, the texture details in the fused images are common blurred due to undesirable artifacts introduced from the different modalities. In this paper, we propose a generic multimodal image fusion framework by combing the visual saliency model and flexible bilevel paradigm. Specifically, we decompose input images into an intensity layer, representing large-scale intensity variations, and a detail layer, containing small textures changes. Then we fuse the intensity layer through visual saliency map to improve the contrast of an image under consideration, and design a bilevel paradigm for fusing the detail layer to obtain fine details. Furthermore, to make the fused result visual friendly, a deep prior is built in the bilevel paradigm. Besides, an elastic target-guided hyper-parameter is introduced to dominate the proportion of the textural details from the source images, which can be further adjusted in accordance with different fusion tasks. We conducted the experiments on three available datasets to demonstrate the superiority of our framework against the state-of-the-art methods quantitatively and qualitatively in a variety of fusion tasks, including infrared and visible image, near-infrared and visible image fusion and multimodal medical image fusion.

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Notes

  1. https://figshare.com/articles/TNOImageFusionDataset/1008029.

  2. http://www.med.harvard.edu/AANLIB/home.html.

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Funding

This paper is funded by Science Foundation of China under Grant (Nos. 61922019, 61733002 and 616721255),LiaoNing Revitalization.

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Authors and Affiliations

  1. International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

    Jinyuan Liu, Zhiying Jiang, Guanyao Wu, Risheng Liu & Xin Fan

  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

    Jinyuan Liu, Zhiying Jiang, Risheng Liu & Xin Fan

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  1. Jinyuan Liu
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  2. Zhiying Jiang
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Correspondence to Xin Fan.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “A Unified Image Fusion Framework with Flexible Bilevel Paradigm Integration.”

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Liu, J., Jiang, Z., Wu, G. et al. A unified image fusion framework with flexible bilevel paradigm integration. Vis Comput 39, 4869–4886 (2023). https://doi.org/10.1007/s00371-022-02633-9

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