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Infrared (IR) Spectroscopy: Overview

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Related Experiment Videos

Infrared and Visible Image Fusion With Language-Driven Loss and Knowledge Distillation.

Yuhao Wang, Lingjuan Miao, Zhiqiang Zhou

    IEEE Transactions on Neural Networks and Learning Systems
    |May 12, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel language-driven approach for infrared and visible image fusion (IVIF), overcoming limitations of traditional methods that rely on mathematical definitions. The new technique leverages natural language to guide the fusion process, achieving superior results and enabling real-time applications.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Infrared and visible image fusion (IVIF) combines complementary data from two modalities.
    • Current deep learning methods for IVIF struggle due to the absence of ground-truth fused images, limiting performance.
    • Existing fusion methods heavily rely on mathematically defined loss functions, which are difficult to optimize without ground truth.

    Purpose of the Study:

    • To develop a novel language-driven approach for IVIF that bypasses the need for explicit mathematical modeling.
    • To enhance fusion performance by utilizing the expressive power of natural language.
    • To enable real-time IVIF applications through efficient model design.

    Main Methods:

    • A comprehensive language-expressed fusion objective was formulated and encoded into a multimodal embedding space using CLIP.
    • A language-driven fusion model was constructed in the embedding space, relating image modalities to the language objective.
    • A language-driven loss function was derived for supervised training, incorporating regularization and patch filtering for robustness.
    • A knowledge distillation framework was proposed for real-time inference, enabling student models to learn from a teacher model.

    Main Results:

    • The proposed language-driven method significantly outperforms existing IVIF techniques.
    • The approach demonstrates high robustness and generalization capabilities.
    • The knowledge distillation framework allows for real-time inference on high-resolution images with comparable performance.

    Conclusions:

    • Language-driven objectives offer a promising alternative to mathematical definitions in image fusion tasks.
    • The developed method effectively improves IVIF performance and addresses practical application challenges.
    • The research provides a robust and efficient solution for real-time infrared and visible image fusion.