Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

Chongyi LI, Saeed ANWAR, Junhui HOU*, Runmin CONG, Chunle GUO, Wenqi REN

*Corresponding author for this work

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

377 Citations (Scopus)


Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at: https://li-chongyi.github.io/Proj_Ucolor.html.

Original languageEnglish
Article number9426457
Pages (from-to)4985-5000
Number of pages16
JournalIEEE Transactions on Image Processing
Early online date21 May 2021
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.


  • color correction
  • image enhancement
  • scattering removal
  • Underwater imaging


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