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Research on an Underwater Visual Enhancement Method Based on Adaptive Parameter Optimization in a Multi-Operator

Zhiyong Yang1,2, Shengze Yang1,2, Yuxuan Fu1,2

  • 1Engineering Research and Design Institute of Agricultural Equipment, Hubei University of Technology, Wuhan 430068, China.

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Summary
This summary is machine-generated.

This study introduces an adaptive framework for enhancing underwater images, improving clarity and color. The method optimizes parameters for better visual quality and feature stability in diverse water conditions.

Keywords:
adaptive parameter optimizationfeature robustnessmulti-operator enhancement frameworkunderwater image enhancement

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Area of Science:

  • Computer Vision
  • Image Processing
  • Underwater Imaging

Background:

  • Underwater images suffer from luminance attenuation, structural degradation, and color distortion.
  • Traditional enhancement methods (e.g., UDCP, HE) are unstable due to variable water conditions.

Purpose of the Study:

  • Propose a multi-operator underwater image enhancement framework with adaptive parameter optimization.
  • Address challenges of luminance, structure, and color degradation in underwater imagery.

Main Methods:

  • Constructed a collaborative pipeline using CLAHE with highlight protection, texture-gated USM, and saturation compensation.
  • Developed an adaptive multi-operator parameter optimization strategy with a unified scoring function.
  • Dynamically adjusted CLAHE clip limit, USM gain, and Gaussian scale based on water conditions.

Main Results:

  • Achieved superior structural clarity and natural color appearance on EUVP and UIEB datasets.
  • Obtained higher quality metrics (AG, UIQM) on the RUIE dataset.
  • Improved ORB feature counts (12.5%), inlier matches (9.3%), and UIQM (3.9%) on UVE38K dataset compared to baseline.
  • Verified geometric consistency and temporal stability of enhanced features.

Conclusions:

  • The proposed adaptive framework effectively enhances underwater images, outperforming conventional methods.
  • Adaptive parameter optimization ensures robust performance across varying underwater environments.
  • Enhanced features exhibit improved geometric consistency and temporal stability for downstream applications.