Underwater images are altered by the physical characteristics of the medium through which light rays pass before reaching the optical sensor. Scattering and wavelength-dependent absorption significantly modify the captured colors depending on the distance of observed elements to the image plane. In this paper, we aim to recover an image of the scene as if the water had no effect on light propagation. We introduce SUCRe, a new method that exploits the scene's 3D structure for underwater color restoration. By following points in multiple images and tracking their intensities at different distances to the sensor, we constrain the optimization of the parameters in an underwater image formation model and retrieve unattenuated pixel intensities. We conduct extensive quantitative and qualitative analyses of our approach in a variety of scenarios ranging from natural light to deep-sea environments using three underwater datasets acquired from real-world scenarios and one synthetic dataset. We also compare the performance of the proposed approach with that of a wide range of existing state-of-the-art methods. The results demonstrate a consistent benefit of exploiting multiple views across a spectrum of objective metrics. Our code is publicly available at https://github.com/clementinboittiaux/sucre.

翻译：水下图像在光线到达光学传感器前，会受到媒介物理特性的影响。散射和波长依赖性吸收会显著改变捕获的颜色，具体变化取决于观察元素到图像平面的距离。本文旨在恢复场景图像，模拟水对光传播无影响的效果。我们提出SUCre方法，这是一种利用场景三维结构进行水下颜色恢复的新技术。通过跟踪多幅图像中的点并监测其在不同传感器距离处的强度变化，我们约束水下图像形成模型中的参数优化，从而复原未衰减的像素强度。我们基于三个真实世界场景数据集和一个合成数据集，在从自然光到深海环境的多类场景中对方法进行了广泛的定量与定性分析，并将所提方法性能与多种现有前沿方法进行了对比。结果表明，利用多视角信息在多种客观指标上具有持续优势。我们的代码已公开于 https://github.com/clementinboittiaux/sucre。