This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based framework built upon SD-Turbo to enhance underwater 4-D LF imaging by leveraging its spatial-angular structure. GeoDiff-LF consists of three key adaptations: (1) a modified U-Net architecture with convolutional and attention adapters to model geometric cues, (2) a geometry-guided loss function using tensor decomposition and progressive weighting to regularize global structure, and (3) an optimized sampling strategy with noise prediction to improve efficiency. By integrating diffusion priors and LF geometry, GeoDiff-LF effectively mitigates color distortion in underwater scenes. Extensive experiments demonstrate that our framework outperforms existing methods across both visual fidelity and quantitative performance, advancing the state-of-the-art in enhancing underwater imaging. The code will be publicly available at https://github.com/linlos1234/GeoDiff-LF.

翻译：本研究针对通过4维光场成像获取高质量水下图像这一具有挑战性的问题展开探索。为此，我们提出GeoDiff-LF，一种基于SD-Turbo的新型扩散框架，通过利用其空间-角度结构来增强水下4维光场成像。GeoDiff-LF包含三项关键适配： (1) 采用配备卷积与注意力适配器的改进型U-Net架构以建模几何线索， (2) 基于张量分解与渐进式加权的几何引导损失函数以正则化全局结构， (3) 结合噪声预测的优化采样策略以提升效率。通过整合扩散先验与光场几何结构，GeoDiff-LF能有效缓解水下场景中的色彩失真问题。大量实验表明，本框架在视觉保真度与量化性能两方面均优于现有方法，推动了水下成像增强领域的前沿进展。代码将公开发布于https://github.com/linlos1234/GeoDiff-LF。