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Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast

Apoorav Maulik Sharma1, Renu Vig1, Ayush Dogra2

  • 1UIET, Panjab University, Chandigarh, India.

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|July 23, 2024
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Summary
This summary is machine-generated.

This study presents a novel multi-stage image fusion framework integrating infrared (IR) and visible (VIS) images for improved low-light performance. The method enhances clarity and detail, offering significant advantages for real-world image analysis applications.

Keywords:
Bayesian fuseIRImage fusionLipschitz constraintsQuadratic contrastSurface from shadeVisible

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Low-light conditions pose significant challenges for image analysis, degrading image quality and obscuring important details.
  • Integrating infrared (IR) and visible (VIS) spectrum images offers a potential solution, but effective fusion techniques are needed.

Purpose of the Study:

  • To introduce an innovative multi-stage image fusion framework for combining IR and VIS images.
  • To enhance image clarity, detail, and edge preservation, particularly in low-light environments.

Main Methods:

  • Preprocessing using an Efficient Guided Image Filter (IR) and contrast/brightness enhancement (VIS).
  • Two-scale decomposition with Lipschitz constraints to separate base and detail layers.
  • Fusion stages employing Bayesian theory for base layers and Surface from Shade (SfS) for detail layers.
  • Choose Max principle for texture selection and final image amalgamation.

Main Results:

  • Demonstrated significant improvements in edge preservation.
  • Achieved notable enhancement in image detail and clarity.
  • Showcased effective noise reduction in fused images.

Conclusions:

  • The proposed multi-stage fusion framework effectively integrates IR and VIS images for superior low-light image analysis.
  • The method offers substantial advantages for various real-world applications requiring high-quality image data.