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UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework.

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    This study introduces UMCFuse, a novel framework for infrared and visible image fusion in complex scenes. UMCFuse enhances detail preservation and interference removal for superior performance in challenging conditions.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Infrared and visible image fusion is crucial but underperforms in complex scenes with interference.
    • Existing methods struggle with detail preservation and noise reduction in challenging environments.

    Purpose of the Study:

    • To develop a unified framework, UMCFuse, for robust infrared and visible image fusion in complex scenes.
    • To improve fusion performance by balancing interference removal and detail preservation.

    Main Methods:

    • Classifying visible image pixels based on light scattering to separate details from intensity.
    • Implementing an adaptive denoising strategy for detail layer fusion.
    • Fusing multi-modal energy features by analyzing them from multiple directions.

    Main Results:

    • UMCFuse demonstrates superior performance over representative methods on complex scene datasets.
    • The method excels in adverse conditions like noise, blur, and overexposure.
    • Effective fusion is shown across various downstream tasks, including segmentation and object detection.

    Conclusions:

    • UMCFuse provides a significant advancement in infrared and visible image fusion for complex scenes.
    • The proposed adaptive denoising and multi-directional feature fusion strategies are key to its success.
    • The framework generalizes well to diverse challenging scenarios and downstream applications.