Abstract: The core issue in underwater optical imaging lies in the scattering effects of water, particularly backscattering, which creates an approximately uniform haze-like background during image formation, severely obscuring the structural information of targets. This significantly limits the effective application of underwater vision systems in high-turbidity environments. To address this challenge, this paper proposes an underwater imaging framework that deeply integrates physical processes with computational imaging methods. The core concept is to transform the originally difficult-to-model problem of global strong scattering into a locally separable problem with well-defined geometric and statistical characteristics, through physical scanning and light field redundancy constraints. In terms of implementation, the framework first decomposes wide-area scattering into localized scattering across sequential frames using line-structured light scanning. Subsequently, virtual aperture technology is employed to preprocess light field data based on structured light geometric priors, thereby constraining scattering regions. Furthermore, the angular redundancy of the light field is utilized to construct Epipolar Plane Images (EPI), and low-rank decomposition is applied to separate the backscattering component, which exhibits low-rank properties, from the target signal, which exhibits sparsity. Finally, high-quality underwater images are obtained through sequential frame stitching and luminance homogenization. System experiments were conducted within a turbidity range of 10–30 NTU (Nephelometric Turbidity Units). The results demonstrate that the proposed method consistently outperforms comparative approaches under various turbidity conditions, showing stable improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and no-reference image quality assessment metrics. Particularly under high-turbidity conditions, the method exhibits stronger robustness, with significantly less degradation in imaging quality as turbidity increases compared to other methods. This validates the effectiveness of the physics-computation collaborative imaging framework in complex scattering environments.

摘要： 水下光学成像面临的核心挑战在于水体散射效应，尤其是后向散射会在成像过程中形成近似均匀的雾化背景，严重掩盖目标结构信息，从而限制水下视觉系统在高浊度环境中的有效应用。针对这一问题，本文构建了一种物理过程与计算成像方法深度融合的水下成像框架，其核心思想在于通过物理扫描与光场冗余约束，将原本难以建模的全局强散射问题转化为具有明确几何与统计特性的局部可分离问题。在具体实现上，首先利用线结构光扫描将广域散射分解为序列帧中的局部散射；随后结合虚拟孔径技术，对光场数据进行基于结构光几何先验的预处理以约束散射区域；进一步利用光场角度冗余性构建极平面图像（Epipolar Plane Image, EPI），并通过低秩分解分离具有低秩特性的后向散射分量与具有稀疏特性的目标信号；最后，通过序列帧拼接与亮度均匀化处理获得完整的高质量水下图像。系统实验在10–30 浊度单位（NTU）范围内开展。实验结果表明，所提出的方法在不同浑浊度条件下均显著优于对比方法，在峰值信噪比、结构相似性及无参考质量评价指标上均取得稳定提升。尤其在高浊度条件下，该方法表现出更强的性能鲁棒性，其成像质量随浊度增加的衰减幅度明显低于对比方法，验证了该物理–计算协同成像框架在复杂散射环境中的有效性。