Abstract
Underwater images often suffer from severe color distortion, low contrast, and a hazy appearance due to wavelength-dependent light absorption and scattering. Simultaneously, existing deep learning models exhibit high computational complexity, which limits their practical deployment for real-time underwater applications. To address these challenges, this paper presents a novel underwater image enhancement model, called Adaptive Frequency Fusion and Illumination Aware Network (AQUA-Net). It integrates a residual encoder decoder with dual auxiliary branches, which operate in the frequency and illumination domains. The frequency fusion encoder enriches spatial representations with frequency cues from the Fourier domain and preserves fine textures and structural details. Inspired by Retinex, the illumination-aware decoder performs adaptive exposure correction through a learned illumination map that separates reflectance from lighting effects. This joint spatial, frequency, and illumination design enables the model to restore color balance, visual contrast, and perceptual realism under diverse underwater conditions. Additionally, we present a high-resolution, real-world underwater video-derived dataset from the Mediterranean Sea, which captures challenging deep-sea conditions with realistic visual degradations to enable robust evaluation and development of deep learning models. Extensive experiments on multiple benchmark datasets show that AQUA-Net performs on par with SOTA in both qualitative and quantitative evaluations while using less number of parameters. Ablation studies further confirm that the frequency and illumination branches provide complementary contributions that improve visibility and color representation. Overall, the proposed model shows strong generalization capability and robustness, and it provides an effective solution for real-world underwater imaging applications.
Abstract (translated)
水下图像常常因为波长依赖的光吸收和散射而出现严重的色彩失真、低对比度及模糊的现象。同时，现有的深度学习模型由于计算复杂性高，在实时水下应用中部署受到限制。为了解决这些问题，本文提出了一种新型的水下图像增强模型，称为自适应频率融合与光照感知网络（AQUA-Net）。该模型结合了残差编码器解码器以及两个辅助分支，分别在频率和光照领域运作。频率融合编码器通过傅里叶域中的频率线索丰富空间表示，并保留精细纹理和结构细节。受Retinex启发，光照感知解码器通过学习到的光照图执行自适应曝光校正，从而分离反射率与照明效果。这种结合了空间、频率和光照的设计使模型能够在各种水下条件下恢复色彩平衡、视觉对比度和感官真实性。 此外，我们还提供了一个高分辨率的真实世界地中海海底视频衍生数据集，该数据集捕捉到了具有挑战性的深海条件以及现实的视觉退化情况，从而能够进行稳健的评估和深度学习模型的发展。在多个基准数据集上的广泛实验表明，在定性和定量评价中，AQUA-Net的表现与最先进方法相当，但参数数量较少。消融研究进一步证实了频率和光照分支提供的互补贡献有助于提高可见度和色彩表现。 总体而言，所提出的模型展示了强大的泛化能力和稳健性，并为实际的水下成像应用提供了一个有效的解决方案。
URL
https://arxiv.org/abs/2512.05960
