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Adaptive fractional multi-scale edge-preserving decomposition and saliency detection fusion algorithm.

Hui Yan1, Xuefeng Zhang1

  • 1School of Sciences, Northeastern University, Shenyang, Liaoning, 110819, China.

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|August 11, 2020
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Summary

This study introduces a new method for fusing infrared (IR) and visible (VIS) images using adaptive fractional multi-scale decomposition. The technique enhances target detection and preserves background details, outperforming existing fusion methods.

Keywords:
Adaptive fractional orderFractional multi-scale edge-preserving decompositionImage fusionInfrared and visible imageSaliency detection

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image fusion combines complementary information from multiple images.
  • Automatic target recognition benefits from expanded space-time detection scope.
  • Infrared (IR) and Visible (VIS) image fusion is crucial for enhanced situational awareness.

Purpose of the Study:

  • To develop an advanced image fusion method for IR and VIS images.
  • To improve automatic target recognition by effectively integrating multi-modal image data.
  • To preserve salient target features and background information during fusion.

Main Methods:

  • Proposed an adaptive fractional multi-scale edge-preserving decomposition within a weighted least square framework.
  • Decomposed fused images into base and detail layers.
  • Utilized an adaptive fractional saliency map from IR images for base layer fusion.
  • Employed a choose-max strategy for detail layer fusion.

Main Results:

  • The proposed method effectively fuses IR and VIS images.
  • Saliency information from IR images is significantly highlighted.
  • Background information is well-preserved in the fused images.
  • Experimental results demonstrate superior performance over state-of-the-art fusion techniques.

Conclusions:

  • The adaptive fractional multi-scale decomposition method offers superior performance in IR and VIS image fusion.
  • The approach enhances target extraction while maintaining background integrity.
  • This method shows significant promise for applications in automatic target recognition and surveillance.