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<p>The fusion of infrared and visible light images is a significant image enhancement technique that leverages the strengths of both infrared and visible light images to achieve high-quality results. However, during the fusion process, information loss is inevitable. To minimize this loss and enhance contrast and detail information, this paper proposes a novel image fusion algorithm based on attention mechanisms, termed Dual-Branch Squared Quadratic (DBSQ)Fusion. This method fully integrates the characteristics of different source images and processes them through specifically designed channels to maximize the retention of important information from the original images. Additionally, Feature Contrast Enhancement Fusion Network(FCEFN) is designed to exploit the differences between infrared and visible light features, enabling information complementarity by separating these distinct features. Furthermore, a Sparse Multi-scale Loss function (SMLoss) is employed to enhance multi-scale information and edge textures, thereby further improving the quality of the fused image. Extensive experiments were conducted on multiple datasets, and various evaluation metrics were utilized to demonstrate the superiority of our proposed model.</p>

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DBSQFusion: a multimodal image fusion method based on dual-channel attention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Shaodong Liu,
  • Faming Shao,
  • Xiaohui He,
  • Jinhong Xue,
  • Heng Zhang,
  • Qing Liu

摘要

The fusion of infrared and visible light images is a significant image enhancement technique that leverages the strengths of both infrared and visible light images to achieve high-quality results. However, during the fusion process, information loss is inevitable. To minimize this loss and enhance contrast and detail information, this paper proposes a novel image fusion algorithm based on attention mechanisms, termed Dual-Branch Squared Quadratic (DBSQ)Fusion. This method fully integrates the characteristics of different source images and processes them through specifically designed channels to maximize the retention of important information from the original images. Additionally, Feature Contrast Enhancement Fusion Network(FCEFN) is designed to exploit the differences between infrared and visible light features, enabling information complementarity by separating these distinct features. Furthermore, a Sparse Multi-scale Loss function (SMLoss) is employed to enhance multi-scale information and edge textures, thereby further improving the quality of the fused image. Extensive experiments were conducted on multiple datasets, and various evaluation metrics were utilized to demonstrate the superiority of our proposed model.