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Infrared and Visible Image Fusion Based on Different Constraints in the Non-Subsampled Shearlet Transform Domain.

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Summary

This study introduces a new infrared and visible image fusion algorithm using non-subsampled shearlet transform (NSST) to enhance image details and target salience, outperforming existing methods.

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Nash equilibriumconstraintsdetails of imageimage fusioninfrared and visible imagessalient targets

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Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Infrared and visible image fusion aims to combine complementary information.
  • Existing fusion methods often suffer from artifacts and lack of detail.
  • Artificial parameters in fusion can degrade image quality.

Purpose of the Study:

  • To propose a novel infrared and visible image fusion algorithm.
  • To overcome the lack of detail and artifacts in fused images.
  • To enhance target salience in the fused output.

Main Methods:

  • Utilized non-subsampled shearlet transform (NSST) for image decomposition into high and low frequency bands.
  • Applied gradient constraint for fusing high-level bands to preserve details.
  • Employed saliency constraint for fusing low-level bands to enhance target visibility.
  • Incorporated Nash equilibrium for coefficient updates before inverse NSST.

Main Results:

  • The proposed method effectively preserves details in the fused images.
  • Targets in the fused images are more salient compared to other methods.
  • Quantitative results demonstrate superior performance in detail retention and target highlighting.

Conclusions:

  • The novel NSST-based fusion algorithm significantly improves image fusion quality.
  • The gradient and saliency constraints effectively address detail loss and target obscurity.
  • This method offers a more effective solution for infrared and visible image fusion.