Underwater images suffer severe degradation due to wavelength-dependent attenuation, scattering, and illumination non-uniformity that vary across water types and depths. We propose an unsupervised Domain-Invariant Visual Enhancement and Restoration (DIVER) framework that integrates empirical correction with physics-guided modeling for robust underwater image enhancement. DIVER first applies either IlluminateNet for adaptive luminance enhancement or a Spectral Equalization Filter for spectral normalization. An Adaptive Optical Correction Module then refines hue and contrast using channel-adaptive filtering, while Hydro-OpticNet employs physics-constrained learning to compensate for backscatter and wavelength-dependent attenuation. The parameters of IlluminateNet and Hydro-OpticNet are optimized via unsupervised learning using a composite loss function. DIVER is evaluated on eight diverse datasets covering shallow, deep, and highly turbid environments, including both naturally low-light and artificially illuminated scenes, using reference and non-reference metrics. While state-of-the-art methods such as WaterNet, UDNet, and Phaseformer perform reasonably in shallow water, their performance degrades in deep, unevenly illuminated, or artificially lit conditions. In contrast, DIVER consistently achieves best or near-best performance across all datasets, demonstrating strong domain-invariant capability. DIVER yields at least a 9% improvement over SOTA methods in UCIQE. On the low-light SeaThru dataset, where color-palette references enable direct evaluation of color restoration, DIVER achieves at least a 4.9% reduction in GPMAE compared to existing methods. Beyond visual quality, DIVER also improves robotic perception by enhancing ORB-based keypoint repeatability and matching performance, confirming its robustness across diverse underwater environments.

翻译：水下图像因波长依赖性衰减、散射及光照不均匀性而严重退化，且这些退化因素随水体类型和深度变化。本文提出一种无监督的领域不变视觉增强与复原（DIVER）框架，该框架将经验校正与物理引导建模相结合，以实现鲁棒的水下图像增强。DIVER首先应用IlluminateNet进行自适应亮度增强或采用光谱均衡滤波器进行光谱归一化。随后，自适应光学校正模块通过通道自适应滤波优化色调与对比度，而Hydro-OpticNet则利用物理约束学习补偿后向散射与波长依赖性衰减。IlluminateNet和Hydro-OpticNet的参数通过使用复合损失函数的无监督学习进行优化。DIVER在八个涵盖浅水、深水及高浑浊环境的多样化数据集上进行了评估，包括自然低光照与人工照明场景，并采用了参考与非参考评价指标。尽管现有先进方法（如WaterNet、UDNet和Phaseformer）在浅水条件下表现尚可，但在深水、不均匀光照或人工照明条件下性能显著下降。相比之下，DIVER在所有数据集上均取得最佳或接近最佳的性能，展现出强大的领域不变能力。在UCIQE指标上，DIVER相比现有最佳方法至少提升9%。在低光照SeaThru数据集上（该数据集通过色板参考支持色彩复原的直接评估），DIVER的GPMAE相比现有方法至少降低4.9%。除视觉质量外，DIVER还通过提升基于ORB的关键点重复性与匹配性能改善了机器人感知能力，证实了其在不同水下环境中的鲁棒性。