An Underwater Image Enhancement Benchmark Dataset and Beyond

Chongyi LI, Chunle GUO, Wenqi REN, Runmin CONG, Junhui HOU, Sam KWONG, Dacheng TAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

927 Citations (Scopus)

Abstract

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 https://li-chongyi.github.io/proj benchmark.html.
Original languageEnglish
Pages (from-to)4376-4389
JournalIEEE Transactions on Image Processing
Volume29
Early online date28 Nov 2019
DOIs
Publication statusPublished - 13 Feb 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the China Computer Federation (CCF)-Tencent Open Fund, in part by the Zhejiang Lab’s International Talent Fund for Young Professionals, in part by the Fundamental Research Funds for the Central Universities under Grant 2019RC039, in part by the National Natural Science Foundation of China under Grant 61802403, Grant 61771334, and Grant 61871342, in part by the Hong Kong Research Grants Council (RGC) General Research Funds (CityU 11205314) under Grant 9042038 and (CityU 11200116) under Grant 9042322, in part by the Hong Kong RGC Early Career Schemes under Grant 9048123, and in part by the China Postdoctoral Science Foundation under Grant 2019M660438.

Keywords

  • comprehensive evaluation
  • deep learning
  • real-world underwater images
  • Underwater image enhancement

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