Abstract
Given the complexity of underwater environments and the variability of water as a medium, underwater images are inevitably subject to various types of degradation. The degradations present nonlinear coupling rather than simple superposition, which renders the effective processing of such coupled degradations particularly challenging. Most existing methods focus on designing specific branches, modules, or strategies for specific degradations, with little attention paid to the potential information embedded in their coupling. Consequently, they struggle to effectively capture and process the nonlinear interactions of multiple degradations from a bottom-up perspective. To address this issue, we propose JDPNet, a joint degradation processing network, that mines and unifies the potential information inherent in coupled degradations within a unified framework. Specifically, we introduce a joint feature-mining module, along with a probabilistic bootstrap distribution strategy, to facilitate effective mining and unified adjustment of coupled degradation features. Furthermore, to balance color, clarity, and contrast, we design a novel AquaBalanceLoss to guide the network in learning from multiple coupled degradation losses. Experiments on six publicly available underwater datasets, as well as two new datasets constructed in this study, show that JDPNet exhibits state-of-the-art performance while offering a better tradeoff between performance, parameter size, and computational cost.
Abstract (translated)
鉴于水下环境的复杂性和作为介质的水的变异性，水下图像不可避免地会遭受各种类型的退化。这些退化的表现是非线性的耦合关系，而非简单的叠加效应，这使得有效处理这种耦合退化尤为困难。大多数现有的方法主要集中在为特定退化设计专门的分支、模块或策略上，并且很少关注其耦合中存在的潜在信息。因此，它们难以从下至上有效地捕捉并处理多种退化的非线性交互作用。为了应对这一挑战，我们提出了JDPNet（联合退化处理网络），该网络在一个统一的框架内挖掘和整合了耦合退化中固有的潜在信息。具体而言，我们引入了一个联合特征挖掘模块以及一种概率引导分布策略，以促进对耦合退化特征的有效挖掘与统一分配调整。此外，为了平衡色彩、清晰度和对比度，我们设计了一种新的AquaBalanceLoss来指导网络从多种耦合退化的损失中学习。 我们在六个公开可用的水下数据集上以及在这项研究中构建的两个新数据集上的实验表明，JDPNet展示了最先进的性能，并且在性能、参数大小和计算成本之间提供了更好的权衡。
URL
https://arxiv.org/abs/2512.20213
