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A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure.

Yuanyuan Li1,2, Yanjing Sun1, Mingyao Zheng2

  • 1School of Information and Electrical, China University of Mining and Technology, Xuzhou 221116, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a new multi-exposure image fusion (MEF) method using adaptive patch structure. The technique enhances image detail and contrast, producing vivid high-dynamic-range (HDR) images with superior visual quality.

Keywords:
adaptive selectionmulti-exposure image fusionpatch structure decompositiontexture information entropy

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Multi-exposure image fusion (MEF) is crucial for combining low-dynamic-range images into high-dynamic-range (HDR) images.
  • Existing methods aim to enhance color and detail while preserving natural visual effects.

Purpose of the Study:

  • To propose a novel MEF method utilizing an adaptive patch structure.
  • To improve local contrast, capture finer details, and generate more vivid HDR images.

Main Methods:

  • The algorithm integrates image cartoon-texture decomposition and image patch structure decomposition.
  • Adaptive patch size selection is guided by image texture entropy.
  • The structural similarity index is employed for intermediate image optimization.

Main Results:

  • The proposed method effectively enhances local contrast and captures intricate details from source images.
  • Experimental results demonstrate the generation of high-quality HDR images with improved visual fidelity.
  • The approach yields superior visual effects compared to existing MEF techniques.

Conclusions:

  • The novel MEF method based on adaptive patch structure offers a significant advancement in HDR image generation.
  • The technique provides a robust solution for producing visually appealing and information-rich HDR images.