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SCFusion: Infrared and Visible Fusion Based on Salient Compensation.

Haipeng Liu1, Meiyan Ma1, Meng Wang1,2

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Entropy (Basel, Switzerland)
|July 29, 2023
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Summary
This summary is machine-generated.

This study introduces a novel salient compensation-based image fusion method to enhance infrared and visible images in low-light conditions. The technique improves target feature description and global scene perception, outperforming existing fusion algorithms.

Keywords:
deep learningimage fusioninfrared and visible imagessalient compensation

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Current deep learning fusion methods struggle with low-light infrared images, resulting in poor detail and contrast.
  • Existing algorithms often fail to adequately leverage complementary information from infrared and visible modalities.

Purpose of the Study:

  • To develop a superior image fusion method for low-light conditions by effectively integrating infrared and visible image characteristics.
  • To address limitations in texture detail, target contrast, and visual perception in current fusion techniques.

Main Methods:

  • Proposed a salient compensation-based fusion method utilizing a multi-scale edge gradient module (MEGB) for texture extraction.
  • Introduced a salient dense residual module (SRDB) for saliency map generation and feature extraction, trained with salient loss.
  • Implemented a spatial bias module (SBM) to effectively fuse global and local image information.

Main Results:

  • Extensive experiments demonstrated significant advantages in describing target features and global scenes compared to existing methods.
  • Ablation studies confirmed the effectiveness of the proposed modules (MEGB, SRDB, SBM).
  • The method showed facilitation for high-level vision tasks, specifically semantic segmentation.

Conclusions:

  • The proposed salient compensation-based fusion method significantly enhances image quality in low-light scenarios.
  • The novel modules effectively extract and fuse multi-modal image information, improving feature representation.
  • This approach offers a robust solution for infrared and visible image fusion and benefits downstream vision tasks.