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Visible and Infrared Image Fusion Using Deep Learning.

Xingchen Zhang, Yiannis Demiris

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    Summary
    This summary is machine-generated.

    This review covers deep learning methods for visible and infrared image fusion (VIF). It details recent advancements, datasets, and evaluation techniques, offering a guide for researchers in this rapidly evolving field.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Visible and Infrared Image Fusion (VIF) is crucial for tasks like object detection and scene segmentation.
    • Deep learning methods have rapidly become dominant in VIF over the past five years.
    • Existing reviews lack a systematic analysis of these advanced deep learning approaches.

    Approach:

    • This paper provides a comprehensive review of deep learning-based VIF methods.
    • It categorizes VIF techniques, including CNN-based, autoencoder-based, GAN-based, and transformer-based approaches.
    • The review details the motivation, taxonomy, development characteristics, datasets, and evaluation metrics for VIF.

    Key Points:

    • Deep learning has revolutionized VIF, offering superior performance.
    • A structured taxonomy of deep learning VIF methods is presented.
    • Current datasets and performance evaluation strategies are discussed.

    Conclusions:

    • This review offers a systematic overview of deep learning-based VIF.
    • It serves as a valuable reference for researchers and newcomers to the VIF field.
    • Future research directions and prospects in VIF are highlighted.