本文已参与「新人创作礼」活动，一起开启掘金创作之路。
- 水下图像开源数据集 RUIE
- 现实世界的水下增强：挑战、基准和解决方案
- Real-world Underwater Enhancement: Challenging, Benchmark and Efficient Solutions
可以从github下载，可以用于水下图像目标检测等
github地址：dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIE-Benchmark
- 对应论文：Paper “Real-world Underwater Enhancement: Challenging, Benchmark and Efficient Solutions” arxiv.org/abs/1901.05…
- 论文摘要如下：
- 水下图像增强是一项重要的低级视觉任务，具有许多应用，近年来提出了许多算法。 这些基于各种假设开发的算法使用不同的数据集和不同的指标从各个方面证明了成功。 在这项工作中，我们建立了一个海底图像捕获系统，并构建了一个大规模的真实世界水下图像增强 (RUIE) 数据集，分为三个子集。 这三个子集针对三个具有挑战性的增强方面，即图像可见性质量、色偏和更高级别的检测
- Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years. These algorithms developed upon various assumptions demonstrate successes from various aspects using different data sets and different metrics. In this work, we setup an undersea image capturing system, and construct a large-scale Real-world Underwater Image Enhancement (RUIE) data set divided into three subsets. The three subsets target at three challenging aspects for enhancement, i.e., image visibility quality, color casts, and higher-level detection/classification, respectively. We conduct extensive and systematic experiments on RUIE to evaluate the effectiveness and limitations of various algorithms to enhance visibility and correct color casts on images with hierarchical categories of degradation. Moreover, underwater image enhancement in practice usually serves as a preprocessing step for mid-level and high-level vision tasks. We thus exploit the object detection performance on enhanced images as a brand new task-specific evaluation criterion. The findings from these evaluations not only confirm what is commonly believed, but also suggest promising solutions and new directions for visibility enhancement, color correction, and object detection on real-world underwater images.
