Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement

NING Zemeng; LIN Sen; LI Xingran

doi:10.11993/j.issn.2096-3920.2023-0131

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NING Zemeng, LIN Sen, LI Xingran. Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0131

Citation:

NING Zemeng, LIN Sen, LI Xingran. Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0131

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Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement

doi: 10.11993/j.issn.2096-3920.2023-0131

NING Zemeng^1
,,
LIN Sen^{1
,
,},
LI Xingran^2
,

1.
School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China
2.
Hohai-Lille College, Hohai University, Nanjing 210098, China

Received Date: 2023-10-22
Accepted Date: 2024-01-05
Rev Recd Date: 2023-12-17

Available Online: 2024-02-07

Abstract

Abstract

Aiming at the problems of color deviation, low contrast and blurring in underwater images, an underwater image enhancement method based on scattering light compensation combined with color preservation and contrast balance is proposed. Firstly, the relative total variational model is used to separate the structure and texture layer. Among them, the color deviation of the structural layer is corrected by defining a compensation coefficient error matrix based on the RGB spatial mapping relationship, and the texture layer is enhanced by filtering separation and fusion to prevent the initial feature loss of the image. Besides, color preservation-contrast limiting adaptive histogram equalization based on the spatial transformation is performed to further improve the contrast and brightness. Finally, the double-stream enhanced results are fused to obtain the output. It is verified by various evaluations on different datasets that the proposed method has better performance in balancing color deviation, enhancing details, and dehazing, which has practical application value in underwater computer vision tasks.
- Underwater image enhancement,
- Color correction,
- Texture enhancement,
- Color preservation-histogram equalization

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References(27)

References

[1]	王丹, 张子玉, 赵金宝, 等. 基于常散射假设和同态滤波的水下图像增强算法[J]. 水下无人系统学报, 2021, 29(2): 210-217. Wang Dan, Zhang Ziyu, Zhao Jinbao, et al. Underwater image enhancement algorithm based on constant scattering assumption and homomorphic filtering[J]. Journal of Unmanned Undersea Systems, 2021, 29(2): 210-217.
[2]	姚鹏, 刘玉会. 基于UNDERWATER-CUT模型的水下图像增强算法[J]. 水下无人系统学报, 2022, 30(5): 605-611. Yao Peng, Liu Yuhui. Underwater image enhancement based on UNDERWATER-CUT model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611.
[3]	Lei X, Wang H, Shen J, et al. Underwater image enhancement based on color correction and complementary dual image multi-scale fusion[J]. Applied Optics, 2022, 61: 5304-14. doi: 10.1364/AO.456368
[4]	He K, Sun J, Tang X. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33: 2341-52. doi: 10.1109/TPAMI.2010.168
[5]	Hou G, Li J, Wang G, et al. A novel dark channel prior guided variational framework for underwater image restoration[J]. Journal of Visual Communication and Image Representation, 2020, 66: 102732. doi: 10.1016/j.jvcir.2019.102732
[6]	Liang Z, Ding X, Wang Y, et al. GUDCP: Generalization of underwater dark channel prior for underwater image restoration[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(7): 4879-84. doi: 10.1109/TCSVT.2021.3114230
[7]	Zhou J, Yan Y, Chu W, et al. Underwater image restoration via backscatter pixel prior and color compensation[J]. Engineering Applications of Artificial Intelligence, 2022, 111: 104785. doi: 10.1016/j.engappai.2022.104785
[8]	Zhang W, Zhuang P, Sun H, et al. Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement[J]. IEEE Transactions on Image Processing, 2022, 31: 3997-4010. doi: 10.1109/TIP.2022.3177129
[9]	Li X, Hou G, Li K, et al. Enhancing underwater image via adaptive color and contrast enhancement, and denoising[J]. Engineering Applications of Artificial Intelligence, 2022, 111: 104759. doi: 10.1016/j.engappai.2022.104759
[10]	Zhang W, Pan X, Xi X, et al. Color Correction and adaptive contrast enhancement for underwater image enhancement[J]. Computers & Electrical Engineering, 2021, 91: 106981.
[11]	Zhuang P, Li C, Wu J, et al. Bayesian retinex underwater image enhancement[J]. Engineering Applications of Artificial Intelligence, 2021, 101: 104171. doi: 10.1016/j.engappai.2021.104171
[12]	Zhuang P, Ding X. Underwater image enhancement using an edge-preserving filtering retinex algorithm[J]. Multimed Tools Appl, 2020, 79: 17257-77. doi: 10.1007/s11042-019-08404-4
[13]	Zhuang P, Wu J, Porikli F, et al. Underwater image enhancement with hyper-laplacian reflectance priors[J]. IEEE Transactions on Image Processing, 2022, 31: 5442-55. doi: 10.1109/TIP.2022.3196546
[14]	Wang Y, Zhang J, Cao Y, et al. A deep CNN method for underwater image enhancement[C]//24th IEEE International Conference on Image Processing(ICIP). Beijing, China: IEEE, 2017: 1382-86.
[15]	Li C, Guo C, Ren W, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2020, 29: 4376-89. doi: 10.1109/TIP.2019.2955241
[16]	Yang M, Hu K, Du Y, et al. Underwater image enhancement based on conditional generative adversarial network[J]. Signal Processing:Image Communication, 2020, 81: 115723. doi: 10.1016/j.image.2019.115723
[17]	林森, 迟凯晨, 唐延东. 基于复原结构与增强纹理融合的水下图像清晰化[J]. 控制与决策, 2022, 37(3): 635-644. Lin Sen, Chi Kaichen, Tang Yandong. Underwater image sharpening based on fusion of restored structure and enhanced texture[J]. Journal of Control and Decision, 2022, 37(3): 635-644.
[18]	Zhou J, We X, Shi J, et al. Underwater image enhancement method with light scattering characteristics[J]. Computers & Electrical Engineering, 2022, 100: 107898.
[19]	Weng C C, Chen H, Fuh C S. A Novel automatic white balance method for digital still cameras[J]. IEEE International Symposium on Circuits and Systems, 2005, 4: 3801-04.
[20]	Lin S, Zhang R, Ning Z, et al. TCRN: A two-step underwater image enhancement network based on triple-color space feature reconstruction[J]. Journal of Marine Science and Engineering, 2023, 11(6): 1221. doi: 10.3390/jmse11061221
[21]	Peng Y, Cao K, Cosman PC. Generalization of the dark channel prior for single image restoration[J]. IEEE Transactions on Image Processing, 2018, 27: 2856-68. doi: 10.1109/TIP.2018.2813092
[22]	Ancuti C O, Ancuti C, De Vleeschouwer, et al. Color balance and fusion for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2018, 27: 379-393. doi: 10.1109/TIP.2017.2759252
[23]	Li C, Tang S, Kwan H, et al. Color correction based on CFA and enhancement based on retinex with dense pixels for underwater images[J]. IEEE Access, 2020, 8: 155732-41. doi: 10.1109/ACCESS.2020.3019354
[24]	Li X, Hou G, Tan L, et al. A hybrid framework for underwater image enhancement[J]. IEEE Access, 2020, 8: 197448-62. doi: 10.1109/ACCESS.2020.3034275
[25]	Naik A, Swarnakar A, Mittal K. Shallow-uwnet: Compressed model for underwater image enhancement[C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 15853-54.
[26]	Chen Y, Pei S. Domain adaptation for underwater image enhancement via content and style separation[J]. IEEE Access, 2022, 10: 90523-34. doi: 10.1109/ACCESS.2022.3201555
[27]	Yang N, Zhong Q, Li K, et al. A reference-free underwater image quality assessment metric in frequency domain[J]. Signal Process, 2021, 94: 116218.