自适应融合多域滤波与色彩补偿，提升水下图像清晰度与自然色还原。
An Adaptive Underwater Image Enhancement Framework via Multi-Domain Fusion and Color Compensation
- 结合光照补偿与多域滤波，分阶段增强图像对比度与细节。
- 在多个基准数据集上优于现有方法，显著改善色彩失真与结构保留。
- 适合水下视觉分析、海洋监测等对图像质量要求高的场景。
水下光学成像受光吸收、散射和色彩失真严重影响，导致可见度下降与图像分析困难。本文提出一种自适应增强框架，集成光照补偿、多域滤波与动态色彩校正。采用结合CLAHE、Gamma校正与Retinex的混合光照补偿策略提升可见度；通过空间域（高斯、双边、引导滤波）与频域（傅里叶、小波）两阶段滤波有效降噪并保留细节；针对色彩失真，设计自适应色彩补偿（ACC）模型，依据光谱衰减与水体类型动态融合RCP、DCP与MUDCP。最后，引入感知引导的色彩平衡机制实现自然色恢复。在多个基准数据集上的实验表明，该框架在对比度增强、色彩校正与结构保持方面均优于当前最优方法，具备良好的鲁棒性，适用于水下成像应用。
原文摘要 · Abstract (English)
Underwater optical imaging is severely degraded by light absorption, scattering, and color distortion, hindering visibility and accurate image analysis. This paper presents an adaptive enhancement framework integrating illumination compensation, multi-domain filtering, and dynamic color correction. A hybrid illumination compensation strategy combining CLAHE, Gamma correction, and Retinex enhances visibility. A two-stage filtering process, including spatial-domain (Gaussian, Bilateral, Guided) and frequency-domain (Fourier, Wavelet) methods, effectively reduces noise while preserving details. To correct color distortion, an adaptive color compensation (ACC) model estimates spectral attenuation and water type to combine RCP, DCP, and MUDCP dynamically. Finally, a perceptually guided color balance mechanism ensures natural color restoration. Experimental results on benchmark datasets demonstrate superior performance over state-of-the-art methods in contrast enhancement, color correction, and structural preservation, making the framework robust for underwater imaging applications.
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