In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conventional 2-D RGB imaging, 4-D LF imaging excels in capturing scenes from multiple perspectives, thereby indirectly embedding geometric information. This intrinsic property is anticipated to effectively address the challenges associated with underwater imaging. By leveraging both explicit and implicit depth cues present in 4-D LF images, we propose a progressive, mutually reinforcing framework for underwater 4-D LF image enhancement and depth estimation. Specifically, our framework explicitly utilizes estimated depth information alongside implicit depth-related dynamic convolutional kernels to modulate output features. The entire framework decomposes this complex task, iteratively optimizing the enhanced image and depth information to progressively achieve optimal enhancement results. More importantly, we construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods, comprising 75 underwater scenes and 3675 high-resolution 2K pairs. To craft vibrant and varied underwater scenes, we build underwater environments with various objects and adopt several types of degradation. Through extensive experimentation, we showcase the potential and superiority of 4-D LF-based underwater imaging vis-a-vis traditional 2-D RGB-based approaches. Moreover, our method effectively corrects color bias and achieves state-of-the-art performance. The dataset and code will be publicly available at https://github.com/linlos1234/LFUIE.

翻译：本文深入探讨四维光场技术，以改善受光吸收、散射及其他挑战困扰的水下成像。相较于传统的二维RGB成像，四维光场成像擅长从多视角捕获场景，从而间接嵌入几何信息。这一固有特性有望有效应对水下成像的相关挑战。通过利用四维光场图像中显性与隐性的深度线索，我们提出了一种渐进式、相互强化的水下四维光场图像增强与深度估计框架。具体而言，该框架显式地利用估计的深度信息，并结合隐式深度相关的动态卷积核来调制输出特征。整个框架将这一复杂任务分解，通过迭代优化增强图像与深度信息，逐步实现最优增强效果。更重要的是，我们构建了首个基于四维光场的水下图像数据集，用于基于学习方法的定量评估与监督训练，该数据集包含75个水下场景和3675对高分辨率2K图像对。为构建生动多样的水下场景，我们搭建了包含多种物体的水下环境，并采用多种退化类型。通过大量实验，我们展示了基于四维光场的水下成像相较于传统二维RGB方法的潜力与优越性。此外，我们的方法能有效校正色彩偏差，并达到最先进的性能水平。数据集与代码将在 https://github.com/linlos1234/LFUIE 公开提供。