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NDFNGO: Enhanced Northern Goshawk Optimization Algorithm for Image Segmentation.

Xiajie Zhao1, Zuowen Bao2, Yu Shao1

  • 1College of Design, Hanyang University, Ansan 15588, Republic of Korea.

Biomimetics (Basel, Switzerland)
|December 24, 2025
PubMed
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This study introduces NDFNGO, a new mural image segmentation method. It significantly improves mural restoration by enhancing image segmentation performance and preserving original details.

Area of Science:

  • Art Conservation Science
  • Computer Vision
  • Optimization Algorithms

Background:

  • Fresco deterioration threatens cultural heritage.
  • Current mural image segmentation methods lack optimal results.
  • Advanced techniques are needed for effective mural restoration.

Purpose of the Study:

  • To introduce a novel mural image segmentation approach, NDFNGO.
  • To enhance mural restoration by improving segmentation performance.
  • To address limitations in existing image segmentation techniques.

Main Methods:

  • Integration of nonlinear differential learning, decay factor, and Fractional-order adaptive learning into the Northern Goshawk Optimization (NGO) algorithm.
  • Enhancing global exploration and exploitation phases for precise segmentation.
Keywords:
decay factorfractional-order adaptive learning strategyimage segmentationnonlinear differential learning strategyoptimization algorithm

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  • Utilizing historical data to refine segmentation quality.

    • Main Results:

    • NDFNGO achieved high victory rates: 95.85% (fitness), 97.9% (PSNR), 97.9% (SSIM), and 95.8% (FSIM).
    • The algorithm demonstrated superior performance in mural image segmentation tasks.
    • Significant retention of original image information was observed.

    Conclusions:

    • The NDFNGO algorithm offers a superior solution for mural image segmentation.
    • This technology effectively addresses the challenge of fresco deterioration.
    • The method aids conservators in protecting cultural heritage.