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Dual decoding generative adversarial networks for infrared image enhancement.

Yang Yu1, Lin Jiang1, Qijun Hu2

  • 1School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan, China.

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|July 2, 2025
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Summary

This study presents a new dual decoding generative adversarial network (2D-GAN) to enhance infrared images degraded by atmospheric radiation. The method improves image quality by preserving details, enhancing texture clarity, and boosting realism.

Keywords:
2D-GANDeep learningEncoder–decoder networkImage enhancement methodsInfrared image

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Infrared imaging is crucial for security, industry, and medicine.
  • Atmospheric thermal radiation significantly degrades infrared image quality, causing issues like reduced contrast and noise.
  • Existing enhancement methods struggle to effectively address these degradation issues.

Purpose of the Study:

  • To introduce a novel infrared image enhancement method using a dual decoding generative adversarial network (2D-GAN).
  • To improve the quality of infrared images by addressing contrast reduction, texture blurring, and non-uniform noise.
  • To enhance detail preservation, texture clarity, and overall image realism.

Main Methods:

  • Utilizing a dual decoding generative adversarial network (2D-GAN) architecture.
  • Implementing internal and external skip connections to improve high-frequency detail transmission and prevent gradient vanishing.
  • Incorporating a cross-layer attention mechanism for adaptive spatial and channel-wise feature weighting.
  • Designing a joint loss function combining pixel-level accuracy, semantic consistency, and global structural coherence.

Main Results:

  • The proposed 2D-GAN method effectively preserves local details through enhanced high-frequency transmission.
  • The cross-layer attention mechanism minimizes information loss and improves texture clarity and structural coherence.
  • The joint loss function enhances image realism and perceptual quality.
  • Experimental results show superior performance compared to existing methods on public datasets, demonstrating excellent enhancement and generalization capabilities.

Conclusions:

  • The novel 2D-GAN method offers a significant advancement in infrared image enhancement.
  • The integrated approach of skip connections, attention mechanisms, and a joint loss function effectively combats image degradation.
  • The method demonstrates strong performance and generalization, making it suitable for various infrared imaging applications.