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

## TL;DR

This paper introduces a new method for improving underwater images by adapting enhancement parameters to different water conditions, resulting in clearer and more natural-looking images.

## Contribution

A novel adaptive multi-operator framework with parameter optimization for underwater image enhancement is proposed.

## Key findings

- The proposed method outperformed traditional methods in structural clarity and color appearance on EUVP and UIEB datasets.
- On RUIE dataset, the method achieved higher quality metrics like AG = 0.5922 and UIQM = 2.095.
- Adaptive optimization improved ORB feature counts, inlier matches, and UIQM by 12.5%, 9.3%, and 3.9% on UVE38K dataset.

## Abstract

Underwater images often suffer from luminance attenuation, structural degradation, and color distortion due to light absorption and scattering in water. The variations in illumination and color distribution across different water bodies further increase the uncertainty of these degradations, making traditional enhancement methods that rely on fixed parameters, such as underwater dark channel prior (UDCP) and histogram equalization (HE), unstable in such scenarios. To address these challenges, this paper proposes a multi-operator underwater image enhancement framework with adaptive parameter optimization. To achieve luminance compensation, structural detail enhancement, and color restoration, a collaborative enhancement pipeline was constructed using contrast-limited adaptive histogram equalization (CLAHE) with highlight protection, texture-gated and threshold-constrained unsharp masking (USM), and mild saturation compensation. Building upon this pipeline, an adaptive multi-operator parameter optimization strategy was developed, where a unified scoring function jointly considers feature gains, geometric consistency of feature matches, image quality metrics, and latency constraints to dynamically adjust the CLAHE clip limit, USM gain, and Gaussian scale under varying water conditions. Subjective visual comparisons and quantitative experiments were conducted on several public underwater datasets. Compared with conventional enhancement methods, the proposed approach achieved superior structural clarity and natural color appearance on the EUVP and UIEB datasets, and obtained higher quality metrics on the RUIE dataset (Average Gradient (AG) = 0.5922, Underwater Image Quality Measure (UIQM) = 2.095). On the UVE38K dataset, the proposed adaptive optimization method improved the oriented FAST and rotated BRIEF (ORB) feature counts by 12.5%, inlier matches by 9.3%, and UIQM by 3.9% over the fixed-parameter baseline, while the adjacent-frame matching visualization and stability metrics such as inlier ratio further verified the geometric consistency and temporal stability of the enhanced features.