Underwater image restoration aims to remove geometric and color distortions due to water refraction, absorption and scattering. Previous studies focus on restoring either color or the geometry, but to our best knowledge, not both. However, in practice it may be cumbersome to address the two rectifications one-by-one. In this paper, we propose NeuroPump, a self-supervised method to simultaneously optimize and rectify underwater geometry and color as if water were pumped out. The key idea is to explicitly model refraction, absorption and scattering in Neural Radiance Field (NeRF) pipeline, such that it not only performs simultaneous geometric and color rectification, but also enables to synthesize novel views and optical effects by controlling the decoupled parameters. In addition, to address issue of lack of real paired ground truth images, we propose an underwater 360 benchmark dataset that has real paired (i.e., with and without water) images. Our method clearly outperforms other baselines both quantitatively and qualitatively.

翻译：水下图像复原旨在消除由水体折射、吸收和散射引起的几何与色彩失真。以往研究多集中于单独恢复色彩或几何形态，据我们所知，尚未实现二者的同步校正。然而在实际应用中，逐项处理两种校正可能颇为繁琐。本文提出NeuroPump——一种自监督方法，可同步优化并校正水下几何与色彩，其效果犹如将水体抽离。该方法的核心思想是在神经辐射场（NeRF）框架中显式建模折射、吸收与散射过程，使其不仅能实现几何与色彩的同步校正，还能通过解耦参数控制来合成新视角图像及光学特效。此外，针对真实配对基准图像数据匮乏的问题，我们构建了包含真实配对（即有无水体环境对比）图像的水下360度基准数据集。定量与定性实验均表明，本方法显著优于其他基线模型。