Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes. To address this limitation, we propose a Universal Underwater Image Restoration method, termed as UniUIR, considering the complex scenario of real-world underwater mixed distortions as an all-in-one manner. To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module. This module enables each expert to identify distinct types of degradation and collaboratively extract task-specific priors while maintaining global feature representation based on linear complexity. Building upon this foundation, to enhance degradation representation and address the task conflicts that arise when handling multiple types of degradation, we introduce the spatial-frequency prior generator. This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts based on image content, thereby improving the accuracy of image restoration. Finally, to more effectively address complex, region-dependent distortions in UIR task, we incorporate depth information derived from a large-scale pre-trained depth prediction model, thereby enabling the network to perceive and leverage depth variations across different image regions to handle localized degradation. Extensive experiments demonstrate that UniUIR can produce more attractive results across qualitative and quantitative comparisons, and shows strong generalization than state-of-the-art methods.

翻译：现有的水下图像复原方法通常仅处理颜色失真或联合处理颜色与雾霾问题，但往往忽略了水下场景中可能出现的更复杂的退化类型。为应对这一局限，我们提出了一种通用水下图像复原方法，称为UniUIR，将真实水下混合退化的复杂场景视为一体化问题进行处理。为解耦退化特定问题并探索水下图像复原任务中多种退化间的相互关联，我们设计了Mamba混合专家模块。该模块使每个专家能够识别不同类型的退化，并在基于线性复杂度保持全局特征表示的同时，协作提取任务特定的先验信息。在此基础上，为增强退化表示并解决处理多种退化类型时出现的任务冲突，我们引入了空间-频率先验生成器。该模块在空间域和频率域提取退化先验信息，并根据图像内容自适应选择最合适的任务特定提示，从而提升图像复原的准确性。最后，为更有效地处理水下图像复原任务中复杂且区域相关的失真，我们整合了来自大规模预训练深度预测模型的深度信息，使网络能够感知并利用不同图像区域的深度变化来处理局部退化。大量实验表明，UniUIR在定性与定量比较中均能产生更具吸引力的结果，并展现出比现有先进方法更强的泛化能力。