[1]ZHANG Ruihang,LIN Sen.Underwater image enhancement based on multicolor space features and physical models[J].CAAI Transactions on Intelligent Systems,2025,20(2):475-485.[doi:10.11992/tis.202312004]
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Underwater image enhancement based on multicolor space features and physical models

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 475-485 Column: 学术论文—人工智能基础 Public date: 2025-03-05
Title:
Underwater image enhancement based on multicolor space features and physical models
Author(s):
ZHANG Ruihang;  LIN Sen
School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China
Keywords:
underwater image enhancement; image formation model; deep learning; multicolor space; feature aggregation; alternate training; algorithm generalization; convolutional neural network
CLC:
TP391.41
DOI:
10.11992/tis.202312004
Abstract:
Underwater intelligent robots are susceptible to interference from suspended particles and light attenuation phenomena when detecting oceanic information, which leads to the degradation of visual images, causing color distortion and blurring of details. An underwater image enhancement method based on multicolor domain features and physical models is proposed to address these issues. First, a multicolor space feature aggregation network is designed to leverage information from different color spaces to aid in color recovery. Then, a generalized white balance algorithm is applied to achieve a more realistic visual performance, and deep learning algorithms are combined with underwater optical imaging models to produce clear images in a data-driven manner. Finally, a multicolor space alternation model is introduced to train the network and optimize parameters across different color spaces. Experiment results demonstrate that this method effectively improves color balance and detail recovery, outperforming classical and novel algorithms. The proposed method meets the image clarity requirements of underwater intelligent robot vision systems in tasks such as feature point matching and saliency detection.
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Last Update: 2025-03-05

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