Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
This work addresses the need for standardized benchmarks in underwater image enhancement, which is crucial for applications in marine research and robotics, though it is incremental as it builds on existing methods with new data and evaluation criteria.
The authors tackled the problem of evaluating underwater image enhancement algorithms by constructing a large-scale real-world dataset (RUIE) and conducting systematic experiments to assess visibility, color correction, and object detection performance, revealing both limitations and promising solutions.
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.