论文标题
水下图像过滤：方法，数据集和评估
Underwater image filtering: methods, datasets and evaluation
论文作者
论文摘要
水下图像因扭曲颜色并减少对比度的光的选择性衰减而降低了。降解范围取决于水类型，物体和相机之间的距离以及物体所在的水面下方的深度。水下图像过滤旨在恢复或增强在水下图像中捕获的物体的外观。恢复方法补偿了实际降解，而增强方法可以改善感知的图像质量或计算机视觉算法的性能。对水下图像过滤方法的兴趣日益加剧，包括用于恢复和增强的基于学习的方法，以及相关的挑战，需要对最新技术进行全面审查。在本文中，我们回顾了过滤方法的设计原理，并重新审视了确定降级原因至关重要的海洋背景。我们讨论了图像形成模型和各种水类型的恢复方法的结果。此外，我们提出了与任务有关的增强方法，并对培训神经网络和方法评估的数据集进行分类。最后，我们讨论评估策略，包括主观测试和质量评估指标。我们通过平台（https://puiqe.eecs.qmul.ac.uk/）对这项调查进行补充，该调查主持了State-of-the-the-The-Art水下过滤方法并促进比较。
Underwater images are degraded by the selective attenuation of light that distorts colours and reduces contrast. The degradation extent depends on the water type, the distance between an object and the camera, and the depth under the water surface the object is at. Underwater image filtering aims to restore or to enhance the appearance of objects captured in an underwater image. Restoration methods compensate for the actual degradation, whereas enhancement methods improve either the perceived image quality or the performance of computer vision algorithms. The growing interest in underwater image filtering methods--including learning-based approaches used for both restoration and enhancement--and the associated challenges call for a comprehensive review of the state of the art. In this paper, we review the design principles of filtering methods and revisit the oceanology background that is fundamental to identify the degradation causes. We discuss image formation models and the results of restoration methods in various water types. Furthermore, we present task-dependent enhancement methods and categorise datasets for training neural networks and for method evaluation. Finally, we discuss evaluation strategies, including subjective tests and quality assessment measures. We complement this survey with a platform ( https://puiqe.eecs.qmul.ac.uk/ ), which hosts state-of-the-art underwater filtering methods and facilitates comparisons.
