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arXiv 2212.09129cs.CV
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SUCRe：利用场景结构进行水下色彩恢复

SUCRe: Leveraging Scene Structure for Underwater Color Restoration

Ifremer（法国海洋开发研究院）；Université de Toulon（土伦大学）；Aix Marseille Univ（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；LIS（信息与系统科学实验室）；COSMER（海洋环境科学与技术研究中心）

Clémentin Boittiaux, Ricard Marxer, Claire Dune, Aurélien Arnaubec, Maxime Ferrera, Vincent Hugel

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AI总结：

针对水下图像受介质物理特性影响出现色彩改变的问题，提出利用场景3D结构的SUCRe方法，通过多视图点追踪约束成像模型参数优化以恢复色彩，多数据集实验验证其性能优于现有方法。

摘要：

水下图像会受到光线到达光学传感器前所穿过介质的物理特性影响。散射和与波长相关的吸收会根据观测元素到像平面的距离，显著改变捕获到的颜色。本文旨在还原出仿佛水对光传播没有影响的场景图像。我们提出了SUCRe，这是一种利用场景三维结构进行水下色彩恢复的新方法。通过追踪多幅图像中的点，并监测它们在不同传感器距离下的强度，我们对水下成像模型中的参数优化施加约束，进而恢复未衰减的像素强度。我们使用3个真实场景采集的水下数据集和1个合成数据集，在从自然光到深海环境的多种场景下，对所提方法进行了全面的定量和定性分析。我们还将该方法的性能与大量现有最先进方法进行了对比。结果表明，在一系列客观指标下，利用多视图的方法始终具备优势。我们的代码已公开在https://github.com/clementinboittiaux/sucre。

英文摘要：

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 novel 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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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