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An underwater attenuation image enhancement method with adaptive color compensation and detail optimization

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Abstract

Aiming at the problems of color compensation bias and loss of detail information in local areas of current underwater image enhancement methods, this paper proposes an underwater attenuation image enhancement method with adaptive color compensation and detail optimization. The method fully considers the attenuation level of each optical channel to guide the color correction based on the attenuation image, and introduces a brightness adjustment method to give the output image a good natural appearance. Gradient-oriented local contrast enhancement and multi-scale edge optimization methods are used to process the color- corrected image separately to obtain two clear images with balanced natural colors, high contrast and good preservation of detail information and then combine with the multi-scale fusion process Artifact-free image fusion is achieved. The experimental results on the UIEB dataset show that the method in this paper improves the UIQM and BRISQUE scores by 14.32% and 8.53%, respectively, and has fewer free parameters, which can effectively enhance attenuated image contrast, detail information and balance image color. In addition, the method in this paper has a complete feature point matching and target detection application, which can be used as a general framework for image preprocessing tasks.

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Data availability

The data that support the findings of this research are available from the corresponding author upon reasonable request.

Code availability

The code used during the current research is available from the corresponding author on reasonable request.

Abbreviations

ACCDO:

An underwater attenuation image enhancement method with adaptive color compensation and detail optimization

AGCC:

Attenuation image-guided color correction

GOCE:

Gradient-oriented local contrast enhancement

MSEO:

Multi-scale edge optimization

\({I^C}(x,y)\) :

Indicates the input image

\({M^C}(x,y)\) :

Indicates attenuation image

\( \gamma \ \) :

Denotes the gamma transform parameter, taking \( \gamma \ \) = 1.2

\({\phi _{{\mathrm{CC}^*}}}\) :

Indicates the attenuation ratio between color channel

\(I_N^{{C^*}}(x,y)\) :

Indicates color compensated image

\(I_U^C(x,y)\) :

Indicates color equalization image

\(I_{wb}^C(x,y)\) :

Indicates brightness adjustment image

\({\nu ^C}(x,y)\) :

Indicates the brightness adjustment weight

\(\varepsilon \ \) :

Indicates the adjustment speed of each channel’s compensation degree and attenuation change, and takes \(\varepsilon \ \) = 8

\(h\ \) :

Denotes the image chunking ratio of the CLAHE algorithm, taking \(h\ \) = 8

\(I_{{wb - \delta }}^L(x,y)\) :

Indicates CLAHE processed image

\(\nabla I_{{wb - \delta }}^L(x,y)\) :

Denotes the gradient map of the CLAHE processed image

\(n\ \) :

Denotes the neighborhood window size in the gray value mean difference calculation, and takes \(n\ \) = 8

\(\eta \ \) :

Noise judgment threshold, take \(\eta = \overline{\nabla I_{wb - \delta }^L} \left( {x,y} \right) + 25 \)

\(\partial \ \) :

Indicates the local contrast enhancement parameter, taking \(\partial \ \) = 1.2

\({I_{wb - \delta }^L}'(x,y)\) :

Indicates local contrast enhanced image

\(\upsilon \ \) :

Denotes the regularization parameter of the guided filter, taking \(\upsilon = 1 \times {10^{ - 6}}\)

\({I{_{{wb - \delta - g}}^L}}'(x,y)\) :

Denotes the output image after \({I_{wb - \delta }^L}'(x,y)\) -guided filtering process

\(I_{{wb - g}}^C(x,y)\) :

Indicates the output image after \(I_{wb}^C(x,y)\) guided filtering

\(I_{{wb - S}}^C(x,y)\) :

Indicates edge-optimized image

\(g_{{wb - \mathrm{low}}}^C(x,y)\) :

Indicates the low-frequency part of \(I_{wb}^C(x,y)\)

\(g_{{wb - \mathrm{high}}}^C(x,y)\) :

Indicates the high-frequency portion of \(I_{wb}^C(x,y)\)

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Acknowledgements

We thank the anonymous reviewers for their constructive comments, which have greatly improved the paper.

Funding

This research was supported in part by Guangxi Science and Technology Base and Talent Project (Guike AD18281038), in part by National Natural Science Foundation of China (51805104) in part by Guangxi Natural Science Foundation Program (2018GXNSFBA281184) , in part by Innovation Project of Guangxi Graduate Education(YCSW2022281), in part by Guangxi Innovation Driven Development Special Fund Project of Guangxi Province, China under Grant (AA18118002-3).

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Author notes

  1. Yanhua Peng, Yipu Yan, Guoyu Chen and Biao Feng have contributed equally to this work.

Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, China

    Yanhua Peng, Yipu Yan, Biao Feng & Xingyu Gao

  2. Technology Center, Guangzhou Institute of Energy Testing, Guangzhou, China

    Guoyu Chen

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  1. Yanhua Peng
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Contributions

YP and YY contributed to the conception of the research. YY, GC and BF performed the experiment. YP and GC contributed significantly to analysis and manuscript preparation. YP, YY and XG performed the data analyses and wrote the manuscript. YP. YY and XG provided funding. All authors helped perform the analysis with constructive discussions.

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Correspondence to Xingyu Gao.

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Peng, Y., Yan, Y., Chen, G. et al. An underwater attenuation image enhancement method with adaptive color compensation and detail optimization. J Supercomput 79, 1544–1570 (2023). https://doi.org/10.1007/s11227-022-04720-z

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