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

By Risheng Liu, Xin Fan, Ming Zhu, Minjun Hou, Zhongxuan Luo. IEEE transactions on circuits and systems for video technology (Print), 2019

748 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e35b97c958ce9897c9b23a0ba30ff11e63b2dee8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunli Zhu
3. Liheng Bian
4. Lintao Peng
5. Chunle Guo
6. Peixian Zhuang
7. Runmin Cong
8. Peigen Luo

Citations in the corpus

• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, background): “and Cyb = 12 (r + g) − b, which are core elements of the Underwater Image Colorfulness Measure (UICM) [55, 49].”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “In the training process Adam optimizer is utilized to optimize the model.”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “We have collected 8018 underwater images, which are sourced from Bubble Vision1, BIOS2 and existing datasets [1, 9, 26, 31, 32].”
• Fast Underwater Image Enhancement for Improved Visual Perception (2019, methodology): “We conduct a similar analysis for Underwater Image Quality Measure (UIQM) [46], [47], which quantifies”
• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, methodology): “2) Quantitative Comparisons: Table I reports shows the AG [36], IE [64], EI [36], UCIQE [66], and CCF [67] scores of different method experiments on the UIQS [14] dataset.”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “These physical model-based methods are either time-consuming or sensitive to the types of underwater images [31].”