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Interpretable General Image Fusion via Scalable Autoregressive Modeling.

Jingwei Xin, Boneng Shi, Zhen Liang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 2, 2026
    PubMed
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    This study introduces High-order Feature AutoRegressive Fusion (HFARFusion), a novel framework for image fusion. HFARFusion enhances interpretability and accuracy by integrating visual autoregressive modeling with explicit high-order fusion strategies.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Advanced image fusion methods utilize sophisticated architectures for feature extraction.
    • Current methods often employ simple fusion strategies, limiting interpretability and accuracy.

    Purpose of the Study:

    • To bridge the gap between implicit feature extraction and explicit modality fusion.
    • To develop a robust and interpretable image fusion framework.

    Main Methods:

    • Incorporated visual autoregressive modeling for image fusion.
    • Developed a scalable feature autoregressive mechanism for low-to-high resolution processing.
    • Embedded an explicit high-order fusion strategy within progressive feature extraction.

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    Main Results:

    • The High-order Feature AutoRegressive Fusion (HFARFusion) framework demonstrated robust performance.
    • Achieved a balance between fusion performance and transparency.
    • Showcased outstanding results in infrared-visible, medical, multi-focus, and multi-exposure image fusion tasks.

    Conclusions:

    • HFARFusion offers a significant advancement in general image fusion tasks.
    • The autoregressive approach enhances both the accuracy and interpretability of image fusion.
    • The proposed method provides a synergistic relationship between implicit learning and explicit fusion.