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Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel

Author

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  • Dachang Zhu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.

Suggested Citation

  • Dachang Zhu, 2023. "Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel," Mathematics, MDPI, vol. 11(6), pages 1-11, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1382-:d:1095341
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    References listed on IDEAS

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    1. Diyuan Li & Junjie Zhao & Jinyin Ma, 2022. "Experimental Studies on Rock Thin-Section Image Classification by Deep Learning-Based Approaches," Mathematics, MDPI, vol. 10(13), pages 1-28, July.
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    Cited by:

    1. Dujuan Zhou & Zhanchuan Cai & Dan He, 2024. "A New Biorthogonal Spline Wavelet-Based K-Layer Network for Underwater Image Enhancement," Mathematics, MDPI, vol. 12(9), pages 1-16, April.

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