Three-Step Color Correction and Multi-scale Contrast Enhancement for Underwater Images

doi:10.3778/j.issn.1673-9418.2411054

Abstract

Abstract: Due to the absorption and scattering effects of underwater media on light, underwater imaging often suffers from color distortion, contrast degradation, and blurred details. An improved underwater image color correction method is proposed, which includes three steps: local adaptive weak channel compensation, histogram stretching, and white balance based on power transformation. For color corrected images, a joint processing method of edge sharpening, local enhancement, and global enhancement is used to fully enhance the feature information of multiple scales in the image. Based on Laplace pyramid decomposition and reconstruction, weighted fusion of contrast enhancement results at multiple scales is performed. The simulation experiment results of underwater images containing Macbeth color cards show that the color difference value between the underwater color card enhanced by the proposed algorithm and the standard color card is smaller than that of all comparison algorithms. The simulation results using the underwater image standard datasets of UCCS, UIQS, and UIEB show that the proposed algorithm can effectively correct color cast in various types of underwater images. The overall brightness and dark area brightness of the images can be reasonably optimized, and the visibility of scene and texture details in the images is significantly better than that of the comparison algorithms. The simulation results are quantitatively evaluated using average gradient, edge strength, information entropy, underwater image quality evaluation index, and CCF comprehensive index as evaluation indicators. The results show that each performance indicator of the proposed algorithm is either optimal or suboptimal among the compared algorithms, and the overall performance is improved by 16.93% compared with the suboptimal algorithm.

Key words: underwater image, image enhancement, color correction, contrast enhancement, multi-scale enhancement

摘要： 由于水下介质对光的吸收和散射效应，水下成像通常存在颜色失真、对比度退化和细节模糊。提出一种改进的水下图像色彩校正方法，处理过程包括局部自适应的弱通道补偿、直方图拉伸与基于幂次变换的白平衡三个步骤。对于色彩校正后的图像，采用边缘锐化、局部增强与全局增强的联合处理方法充分增强图像中多种尺度的特征信息。基于拉普拉斯金字塔的分解与重构，对多种尺度的对比度增强结果进行加权融合。包含Macbeth色卡的水下图像仿真实验结果显示，采用该算法增强的水下色卡图像与标准色卡的色差值小于所有对比算法。采用水下图像标准数据集UCCS、UIQS与UIEB的仿真实验结果显示，该算法能够有效校正各种类型水下图像的色偏，图像整体亮度与暗区亮度能够得到合理优化，图像的场景细节与纹理细节的可见度明显优于对比算法。采用平均梯度、边缘强度、信息熵、水下图像质量评价指数与CCF综合指数作为评价指标，对仿真结果进行了定量评估。结果表明，该算法的各项性能在对比算法中均为最优或次优，整体性能分值比次优算法提高了16.93%。

关键词: 水下图像, 图像增强, 色彩校正, 对比度增强, 多尺度增强

JIANG Julang, ZHANG Gaoxing, XU Guanghao, LIU Juan. Three-Step Color Correction and Multi-scale Contrast Enhancement for Underwater Images[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(10): 2739-2754.

江巨浪, 张高兴, 徐光豪, 刘娟. 水下图像的三步式色彩校正与多尺度对比度增强[J]. 计算机科学与探索, 2025, 19(10): 2739-2754.

References

[1] WANG Y, SONG W, FORTINO G, et al. An experimental-based review of image enhancement and image restoration methods for underwater imaging[J]. IEEE Access, 2019, 7: 140233-140251.
[2] 宋巍, 肖毅, 杜艳玲, 等. 双流网络的水下视频客观质量评价模型[J]. 计算机科学与探索, 2023, 17(2): 409-418.
SONG W, XIAO Y, DU Y L, et al. Objective underwater video quality assessment model via two-stream networks[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(2): 409-418.
[3] LI C Y, GUO J C, GUO C L. Emerging from water: underwater image color correction based on weakly supervised color transfer[J]. IEEE Signal Processing Letters, 2018, 25(3): 323-327.
[4] 王欣, 石慧. DRHA-UIE: 基于双重残差混合注意力模块的水下图像增强方法[J]. 电子学报, 2023, 51(9): 2398-2407.
WANG X, SHI H. DRHA-UIE: an underwater image enhancement method based on dual residual hybrid attention block[J]. Acta Electronica Sinica, 2023, 51(9): 2398-2407.
[5] HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 1956-1963.
[6] DREWS P L J, NASCIMENTO E R, BOTELHO S S C, et al. Underwater depth estimation and image restoration based on single images[J]. IEEE Computer Graphics and Applications, 2016, 36(2): 24-35.
[7] PENG Y T, COSMAN P C. Underwater image restoration based on image blurriness and light absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4): 1579-1594.
[8] ZHUANG P X, LI C Y, WU J M. Bayesian retinex underwater image enhancement[J]. Engineering Applications of Artificial Intelligence, 2021, 101: 104171.
[9] ZHUANG P X, WU J M, PORIKLI F, et al. Underwater image enhancement with hyper-Laplacian reflectance priors[J]. IEEE Transactions on Image Processing, 2022, 31: 5442-5455.
[10] ABDULLAH-AL-WADUD M, KABIR M H, ALI AKBER DEWAN M, et al. A dynamic histogram equalization for image contrast enhancement[J]. IEEE Transactions on Consumer Electronics, 2007, 53(2): 593-600.
[11] 李华昆, 李恒, 赵磊, 等. 改进的伽马校正与多尺度融合的水下图像增强[J]. 光电子·激光, 2022, 33(7): 700-708.
LI H K, LI H, ZHAO L, et al. Underwater image enhancement based on improved Gamma correction and multi-scale fusion[J]. Journal of Optoelectronics·Laser, 2022, 33(7): 700-708.
[12] LUO W L, DUAN S Q, ZHENG J W. Underwater image restoration and enhancement based on a fusion algorithm with color balance, contrast optimization, and histogram stretching[J]. IEEE Access, 2021, 9: 31792-31804.
[13] BAI L F, ZHANG W D, PAN X P, et al. Underwater image enhancement based on global and local equalization of histogram and dual-image multi-scale fusion[J]. IEEE Access, 2020, 8: 128973-128990.
[14] ZHANG W D, WANG Y D, LI C Y. Underwater image enhancement by attenuated color channel correction and detail preserved contrast enhancement[J]. IEEE Journal of Oceanic Engineering, 2022, 47(3): 718-735.
[15] XIANG D, WANG H H, HE D Y, et al. Research on histogram equalization algorithm based on optimized adaptive quadruple segmentation and cropping of underwater image (AQSCHE)[J]. IEEE Access, 2023, 11: 69356-69365.
[16] JIN S L, QU P X, ZHENG Y, et al. Color correction and local contrast enhancement for underwater image enhancement[J]. IEEE Access, 2022, 10: 119193-119205.
[17] ZHOU J C, PANG L, ZHANG D H, et al. Underwater image enhancement method via multi-interval subhistogram perspective equalization[J]. IEEE Journal of Oceanic Engineering, 2023, 48(2): 474-488.
[18] ZHANG W D, ZHUANG P X, SUN H 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.
[19] ZHANG W D, JIN S L, ZHUANG P X, et al. Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement[J]. IEEE Signal Processing Letters, 2023, 30: 229-233.
[20] ANCUTI C O, ANCUTI C, DE VLEESCHOUWER C, et al. Color balance and fusion for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2018, 27(1): 379-393.
[21] IQBAL M, RIAZ M M, SOHAIB ALI S, et al. Underwater image enhancement using Laplace decomposition[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19: 1500105.
[22] CHEN Q, ZHANG Z, LI G L. Underwater image enhancement based on color balance and multi-scale fusion[J]. IEEE Photonics Journal, 2022, 14(6): 3963010.
[23] YIN M, DU X X, LIU W, et al. Multiscale fusion algorithm for underwater image enhancement based on color preservation[J]. IEEE Sensors Journal, 2023, 23(7): 7728-7740.
[24] ZHANG W D, ZHOU L, ZHUANG P X, et al. Underwater image enhancement via weighted wavelet visual perception fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(4): 2469-2483.
[25] LI J, SKINNER K A, EUSTICE R M, et al. WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images[J]. IEEE Robotics and Automation Letters, 2018, 3(1): 387-394.
[26] QI Q, LI K Q, ZHENG H Y, et al. SGUIE-net: semantic attention guided underwater image enhancement with multi-scale perception[J]. IEEE Transactions on Image Processing, 2022, 31: 6816-6830.
[27] FU Z Q, LIN H X, YANG Y, et al. Unsupervised underwater image restoration: from a homology perspective[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(1): 643-651.
[28] LIU S B, FAN H J, LIN S, et al. Adaptive learning attention network for underwater image enhancement[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 5326-5333.
[29] BAKHT A B, JIA Z K, DIN M U, et al. MuLA-GAN: multi-level attention GAN for enhanced underwater visibility[J]. Ecological Informatics, 2024, 81: 102631.
[30] PARK C W, EOM I K. Underwater image enhancement using adaptive standardization and normalization networks[J]. Engineering Applications of Artificial Intelligence, 2024, 127: 107445.
[31] HUANG D M, WANG Y, SONG W, et al. Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition[C]//Proceedings of the 24th International Conference on Multimedia Modeling. Cham: Springer, 2018: 453-465.
[32] LASHKOV I, YUAN R Z, ZHANG G H. Edge-computing-facilitated nighttime vehicle detection investigations with CLAHE-enhanced images[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 13370-13383.
[33] OGATA M, TSUCHIYA T, KUBOZONO T, et al. Dynamic range compression based on illumination compensation[J]. IEEE Transactions on Consumer Electronics, 2001, 47(3): 548-558.
[34] LIU R S, FAN X, ZHU M, et al. Real-world underwater enhancement: challenges, benchmarks, and solutions under natural light[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(12): 4861-4875.
[35] LI C Y, GUO C L, REN W Q, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2019, 29: 4376-4389.
[36] SHARMA G, WU W C, DALAL E N. The CIEDE2000 color- difference formula: implementation notes, supplementary test data, and mathematical observations[J]. Color Research & Application, 2005, 30(1): 21-30.
[37] MOHD AZMI K Z, ABDUL GHANI A S, MD YUSOF Z, et al. Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm[J]. Applied Soft Computing, 2019, 85: 105810.
[38] CHAN R, ROTTMANN M, GOTTSCHALK H. Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 5108-5117.
[39] PANETTA K, GAO C, AGAIAN S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 2016, 41(3): 541-551.
[40] WANG Y, LI N, LI Z Y, et al. An imaging-inspired no-reference underwater color image quality assessment metric[J]. Computers & Electrical Engineering, 2018, 70: 904-913.