Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light

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....

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2020-12, Vol.30 (12), p.4861-4875
Main Authors: Liu, Risheng, Fan, Xin, Zhu, Ming, Hou, Minjun, Luo, Zhongxuan
Format: Article
Language:English
Subjects:
Algorithms
benchmark
Benchmark testing
Benchmarks
Color
color cast
Datasets
Degradation
Histograms
Image classification
Image color analysis
Image enhancement
Image quality
Imaging
Natural lighting
object detection
Object recognition
Task analysis
Undersea
Underwater construction
Underwater image enhancement
Visibility
Vision
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Summary: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. The benchmark is available at: https://github.com/dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIE-Benchmark.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2019.2963772