一次修复水下图像的几何畸变与颜色失真，像抽干水一样还原真实场景。
NeuroPump: Simultaneous Geometric and Color Rectification for Underwater Images
- 在NeRF中建模折射、吸收和散射，实现几何与色彩同步修复。
- 在自监督条件下，修复效果优于现有方法，且能生成新视角图像。
- 构建首个真实配对的360°水下数据集，支持无真值训练与评估。
水下图像复原旨在消除因水体折射、吸收和散射引起的几何与颜色失真。以往研究通常仅关注颜色或几何修复，但尚未有方法同时处理二者。实际应用中分步修复效率低。本文提出NeuroPump，一种自监督方法，通过在神经辐射场（NeRF）框架中显式建模折射、吸收和散射，实现几何与颜色的同步优化，仿佛将水“抽干”。该方法不仅能同时修复失真，还可通过解耦参数合成新视角与光学效果。针对缺乏真实配对图像的问题，我们构建了首个真实配对的360°水下基准数据集。实验表明，该方法在定量与定性上均显著优于现有基线。
原文摘要 · Abstract (English)
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. Our project page is available at: https://ygswu.github.io/NeuroPump.github.io/.
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