Abstract
Underwater images typically suffer from severe colour distortions, low visibility, and reduced structural clarity due to complex optical effects such as scattering and absorption, which greatly degrade their visual quality and limit the performance of downstream visual perception tasks. Existing enhancement methods often struggle to adaptively handle diverse degradation conditions and fail to leverage underwater-specific physical priors effectively. In this paper, we propose a degradation-aware conditional diffusion model to enhance underwater images adaptively and robustly. Given a degraded underwater image as input, we first predict its degradation level using a lightweight dual-stream convolutional network, generating a continuous degradation score as semantic guidance. Based on this score, we introduce a novel conditional diffusion-based restoration network with a Swin UNet backbone, enabling adaptive noise scheduling and hierarchical feature refinement. To incorporate underwater-specific physical priors, we further propose a degradation-guided adaptive feature fusion module and a hybrid loss function that combines perceptual consistency, histogram matching, and feature-level contrast. Comprehensive experiments on benchmark datasets demonstrate that our method effectively restores underwater images with superior colour fidelity, perceptual quality, and structural details. Compared with SOTA approaches, our framework achieves significant improvements in both quantitative metrics and qualitative visual assessments.
Abstract (translated)
水下图像通常会因散射和吸收等复杂光学效应而遭受严重的色彩失真、低可见度以及结构清晰度降低，这极大地损害了它们的视觉质量和下游视觉感知任务的表现。现有的增强方法往往难以适应各种退化条件，并且无法有效地利用特定于水下的物理先验知识。 在本文中，我们提出了一种基于退化意识的条件扩散模型，以自适应和稳健的方式提升水下图像的质量。给定一张受损的水下图像作为输入，首先使用轻量级双流卷积网络预测其退化程度，并生成一个连续的退化分数作为语义指导。在此基础上，我们引入了一个基于条件扩散的新式恢复网络，该网络以Swin UNet为骨干结构，实现了自适应噪声调度和分层特征细化。 为了将特定于水下的物理先验知识纳入考虑，我们还提出了一种退化引导的自适应特征融合模块以及一个结合了感知一致性、直方图匹配及特征级对比度的混合损失函数。 在基准数据集上的全面实验表明，我们的方法能够有效地恢复水下图像，使其色彩保真度、感知质量和结构细节达到优异水平。与最先进的方法相比，在定量指标和定性视觉评估中，我们框架均取得了显著改进。
URL
https://arxiv.org/abs/2507.22501
