Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Weidong ZHANG, Peixian ZHUANG, Hai-Han SUN, Guohou LI, Sam KWONG, Chongyi LI

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

216 Citations (Scopus)


Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within Is for processing an image of size 1024 x 1024 x 3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github. io/proj_MMLE.html.
Original languageEnglish
Pages (from-to)3997-4010
JournalIEEE Transactions on Image Processing
Early online date3 Jun 2022
Publication statusPublished - 2022
Externally publishedYes

Bibliographical note

This work was supported in part by the China Postdoctoral Science Foundation under Grant 2019M660438; in part by the National Natural Science Foundation of China under Grant 62171252, Grant 61701245, Grant 62071272, Grant 61701247, and Grant 62001158; in part by the Postdoctoral Science Foundation of China under Grant 2021M701903; in part by the National Key Research and Development Program of China under Grant 2020AAA0130000; and in part by the MindSpore, Compute Architecture for Neural Networks (CANN), and Ascend Artificial Intelligence (AI) Processor.


  • color correction
  • contrast enhancement
  • light scattering
  • Underwater image enhancement
  • underwater imaging


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