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Adaptive Image Enhancement Algorithm Based on Variable Step Fruit Fly Optimization Algorithm and Nonlinear Beta

Huajuan Huang1, Dao Tao2, Xiuxi Wei1

  • 1College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.

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|May 23, 2023
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Summary
This summary is machine-generated.

This study introduces an adaptive image enhancement algorithm using a variable step size fruit fly optimization algorithm (VFOA) to automatically adjust nonlinear beta transform parameters. The VFOA-Beta method significantly improves image quality and visual effects compared to existing algorithms.

Keywords:
beta transformfruit fly optimization algorithmfunction testimage enhancementvariable step size

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Traditional manual parameter adjustment for nonlinear beta transform is inefficient and unstable.
  • Automated optimization is needed for improved image enhancement.

Purpose of the Study:

  • To develop an adaptive image enhancement algorithm using an optimized nonlinear beta transform.
  • To improve the efficiency and stability of image parameter adjustment.

Main Methods:

  • Introduced a dynamic step size mechanism into the fruit fly optimization algorithm (FOA) to create a variable step size fruit fly optimization algorithm (VFOA).
  • Combined VFOA with nonlinear beta transform, using image gray variance as the fitness function for parameter optimization.
  • Developed the VFOA-Beta adaptive image enhancement algorithm.

Main Results:

  • The VFOA-Beta algorithm demonstrated significant image enhancement capabilities.
  • Experimental comparisons showed superior visual effects compared to seven other algorithms.
  • The proposed method achieved better image enhancement outcomes.

Conclusions:

  • The VFOA-Beta algorithm offers an effective and automated approach to image enhancement.
  • The method provides practical application value for improving image quality.
  • Intelligent optimization of nonlinear beta transform parameters leads to superior results.