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Rethinking Infrared and Visible Image Fusion from a Heterogeneous Content Synergistic Perception Perspective.

Minxian Shen1, Gongrui Huang1, Mingye Ju1

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|August 14, 2025
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Summary
This summary is machine-generated.

HCSPNet, a novel generative adversarial network (GAN) framework, enhances infrared and visible image fusion (IVIF) by using heterogeneous dual discriminators. This method reduces artifacts and preserves crucial details from both image types.

Keywords:
generative adversarial networkheterogeneous dual discriminatorsinformation distillationinfrared and visible image fusion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Infrared and visible image fusion (IVIF) combines thermal and textural details for richer outputs.
  • Generative adversarial networks (GANs) show promise for IVIF but often suffer from artifacts due to homogeneous discriminators.

Purpose of the Study:

  • To introduce HCSPNet, a novel GAN-based framework designed to overcome limitations in existing IVIF methods.
  • To improve the quality and reduce artifacts in fused infrared and visible images.

Main Methods:

  • HCSPNet utilizes heterogeneous dual discriminators, each tailored for infrared and visible image properties.
  • Adaptive salient information distillation (ASID) modules are integrated into discriminators to focus on critical thermal and textural details.
  • The framework is designed for robust information preservation, even under image degradation.

Main Results:

  • Experiments demonstrate HCSPNet's superior performance and generalization capabilities across multiple datasets.
  • The proposed heterogeneous dual discriminators effectively reduce unwanted artifacts in fused images.
  • The heterogeneous dual discriminators function as a plug-and-play module to enhance existing GAN-based fusion methods.

Conclusions:

  • HCSPNet offers a significant advancement in infrared and visible image fusion.
  • The framework's design ensures high-quality fused images with preserved details and minimal artifacts.
  • The heterogeneous dual discriminator approach holds potential for practical applications in image fusion.