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non-reference underwater image quality

Non-reference underwater image quality refers to the visual fidelity, clarity, and perceptual condition of an underwater image evaluated without comparing it to a distortion-free ground-truth reference image. In real-world aquatic imaging and marine robotics, obtaining an ideal pristine reference is generally impossible due to severe optical distortions caused by wavelength-dependent light absorption, scattering, color casting, and turbidity. As a result, non-reference quality evaluation relies on analyzing the intrinsic statistical characteristics of the degraded image itself, such as measures of sharpness, colorfulness, contrast, and structural information, or on deep learning models trained to score visual quality and gauge the success of underwater image restoration and enhancement algorithms.

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An Underwater Image Enhancement Benchmark Dataset and Beyond

An Underwater Image Enhancement Benchmark Dataset and Beyond

Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, Dacheng Tao

OrganizationsBeijing Jiaotong UniversityCity University of Hong KongInstitute of Information Engineering, Chinese Academy of SciencesTianjin UniversityUniversity of Sydney

Why you should read this

Establishes a standardized real-world benchmark dataset containing 950 underwater images alongside a convolutional baseline network, Water-Net, to rigorously evaluate and advance underwater image restoration.

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at this https URL.

Added

2026-09-18