Abstract
Due to the complex interplay of light absorption and scattering in the underwater environment, underwater images experience significant degradation. This research presents a two-stage underwater image enhancement network called the Data-Driven and Physical Parameters Fusion Network (DPF-Net), which harnesses the robustness of physical imaging models alongside the generality and efficiency of data-driven methods. We first train a physical parameter estimate module using synthetic datasets to guarantee the trustworthiness of the physical parameters, rather than solely learning the fitting relationship between raw and reference images by the application of the imaging equation, as is common in prior studies. This module is subsequently trained in conjunction with an enhancement network, where the estimated physical parameters are integrated into a data-driven model within the embedding space. To maintain the uniformity of the restoration process amid underwater imaging degradation, we propose a physics-based degradation consistency loss. Additionally, we suggest an innovative weak reference loss term utilizing the entire dataset, which alleviates our model's reliance on the quality of individual reference images. Our proposed DPF-Net demonstrates superior performance compared to other benchmark methods across multiple test sets, achieving state-of-the-art results. The source code and pre-trained models are available on the project home page: this https URL.
Abstract (translated)
由于水下环境中光的吸收和散射的复杂相互作用，水下图像会经历显著的质量退化。这项研究提出了一种两阶段的水下图像增强网络——数据驱动与物理参数融合网络（DPF-Net），该网络结合了物理成像模型的稳健性和基于数据的方法的通用性及高效性。 我们首先使用合成数据集训练一个物理参数估计模块，以确保所获得物理参数的真实有效性，而不是仅仅通过应用成像方程来学习原始图像和参考图像之间的拟合关系（如先前研究中常见的做法）。随后，在增强网络与该物理参数估计模块共同训练的过程中，将估算出的物理参数整合到嵌入空间中的数据驱动模型内。 为在水下成像退化过程中保持恢复过程的一致性，我们提出了一种基于物理学的降解一致性损失。此外，我们还建议利用整个数据集创新地引入一个弱参考损失项，以减轻我们的模型对单个参考图像质量依赖的问题。 我们在多个测试集合上验证了所提出的DPF-Net方法，其性能优于其他基准方法，并且达到了最先进的水平。该项目的源代码和预训练模型可在项目主页：[此链接]（请将URL替换为实际提供的链接）获取。
URL
https://arxiv.org/abs/2503.12470
