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A Precise Multi-Exposure Image Fusion Method Based on Low-level Features.

Guanqiu Qi1, Liang Chang2, Yaqin Luo2

  • 1Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.

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|March 19, 2020
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Summary
This summary is machine-generated.

This study introduces a feature patch method (FPM) for multi-exposure image fusion (MEF) to enhance ghost removal in dynamic scenes. The FPM improves image quality, color, and detail processing compared to existing methods.

Keywords:
a priori exposure qualityghost removalhigh-dynamic-range imagingimage fusionmulti-exposure image fusion

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Multi-exposure image fusion (MEF) generates high-dynamic-range (HDR) images.
  • Existing MEF methods struggle with ghost removal in dynamic scenes.

Purpose of the Study:

  • Propose a precise MEF method (FPM) for robust ghost removal in dynamic scenes.
  • Improve overall image quality, including color saturation, sharpness, and local details.

Main Methods:

  • Feature Patch Method (FPM) utilizing a reference image selected by exposure quality.
  • Structure consistency test for ghosting issues in dynamic scenes.
  • Image decomposition into base and detail layers using a guided filter for fusion.

Main Results:

  • FPM significantly improves ghost removal robustness in dynamic scenes.
  • Outperforms six existing MEF methods in color saturation, sharpness, and local detail processing.
  • Achieves precise MEF by integrating structure decomposition and exposure evaluation.

Conclusions:

  • The proposed FPM offers a robust solution for MEF in dynamic scenes.
  • FPM enhances ghost removal while maintaining high image quality.
  • This method advances HDR image generation from dynamic scene captures.