Two-Branch Deep Neural Network for Underwater Image Enhancement in HSV Color Space

Junkang HU, Qiuping JIANG*, Runmin CONG, Wei GAO, Feng SHAO

*Corresponding author for this work

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

40 Citations (Scopus)


Due to the influence of light absorption and scattering, underwater images usually suffer from quality deteriorations such as color cast and reduced contrast. The diverse quality degradations not only dissatisfy the user expectation but also lead to a significant performance drop in many underwater vision applications. This letter proposes a novel two-branch deep neural network for underwater image enhancement (UIE), which is capable of separately removing color cast and enhancing image contrast by fully leveraging useful properties of the HSV color space in disentangling chrominance and intensity. Specifically, the input underwater image is first converted into the HSV color space and disentangled into HS and V channels to serve as the input of the two branches, respectively. Then, the color cast removal branch enhances the H and S channels with a generative adversarial network architecture while the contrast enhancement branch enhances the V channel via a traditional convolutional neural network. The enhanced channels by the two branches are merged and converted back into RGB color space to obtain the final enhanced result. Experimental results demonstrate that, compared with state-of-The-Art UIE methods, our method can produce much more visually pleasing enhanced results.

Original languageEnglish
Pages (from-to)2152-2156
Number of pages5
JournalIEEE Signal Processing Letters
Early online date26 Jul 2021
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1994-2012 IEEE.


  • convolutional neural network
  • deep learning
  • image enhancement
  • Underwater image


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