Color restoration of underwater images using color compensation and convolutional neural network based defogging model

doi:10.13474/j.cnki.11-2246.2024.1112

Abstract

Abstract: Underwater vision measurement has important applications in marine surveying, underwater engineering surveying, underwater archaeology and underwater environmental monitoring. However, underwater images suffer from color distortion, image blurring and low contrast, which limits the application of underwater visual measurement technology in practical environments. A color restoration method for underwater images based on color compensation and convolutional neural network (CNN) defogging model is proposed in this paper, in which the image enhancement is carried out step-by-step.Firstly,the color deviation of underwater images is analyzed, and then an adaptive color compensation strategy combined with the grayscale world white balance algorithm is used to correct underwater image color. Secondly, a CNN based dehazing model was designed to achieve dehazing processing of underwater images. Finally, the adaptive histogram equalization CLAHE method is used to enhance the contrast of underwater images. In order to prove the applicability and superiority of the proposed method, two image datasets are combined to study, and several known underwater image enhancement and restoration methods are compared. The proposed method and several compared methods are evaluated in two aspects of subjective visual effect and quantitative evaluation index. The comparison results show that compared with other enhancement algorithms, the proposed method successfully improves the clarity of the image and reduces the color deviation of the damaged underwater image when processing underwater images in various environments and has superior image color recovery compared with existing enhancement methods.

Key words: underwater image, color compensation, convolutional neural network, defogging, histogram equalization, color restoration

CLC Number:

P229

MA Zhenling, CHEN Yuan, FAN Chengcheng, PAN Yan. Color restoration of underwater images using color compensation and convolutional neural network based defogging model[J]. Bulletin of Surveying and Mapping, 2024, 0(11): 68-73.

References

[1] 郝志成, 吴川, 杨航, 等.基于双边纹理滤波的图像细节增强方法[J].中国光学, 2016, 9(4):423-431.
[2] 郭士祥, 范铀, 范冲.结合图像增强和恢复的增强现实跟踪注册[J].测绘通报, 2020(8):39-43, 64.
[3] 李亚丽, 张松林, 韩杰.基于小波系数分割的局部自适应阈值图像去噪[J].测绘通报, 2020(5):43-46, 122.
[4] 巩龙翔.基于色彩补偿及亮度均衡的水下图像融合算法研究[D].济南:齐鲁工业大学, 2022.
[5] 赵德安, 刘晓洋, 孙月平, 等.基于机器视觉的水下河蟹识别方法[J].农业机械学报, 2019, 50(3):151-158.
[6] JAFFE J S.Computer modeling and the design of optimal underwater imaging systems[J].IEEE Journal of Oceanic Engineering, 1990, 15(2):101-111.
[7] 张莉云.水下图像清晰化算法研究[D].天津:天津大学, 2017.
[8] 任晓奎, 乐明杰, 赵振禹, 等.改进的雾线先验透射率优化图像去雾算法[J].电光与控制, 2023, 30(6):47-54.
[9] 朱钦权, 王同罕, 贾惠珍.基于雾密度感知增强的去雾图像质量评价方法[J].计算机工程, 2023, 49(6):208-216, 226.
[10] 郑建铧, 郝重阳, 雷方元, 等.利用色彩直方图特征进行偏色图象的自动检测和校正[J].中国图象图形学报(A辑), 2003(9):1001-1007.
[11] 李治江.彩色影像色调重建的理论与实践[D].武汉:武汉大学, 2005:61-62.
[12] ZHU Shidong, LUO Weilin, DUAN Shunqiang.Enhancement of underwater images by CNN-based color balance and dehazing[J].Electronics, 2022, 11(16):2537.
[13] LI Chongyi, GUO Chunle, REN Wenqi, et al.An underwater image enhancement benchmark dataset and beyond[J].IEEE Transactions on Image Processing, 2020, 29:4376-4389.
[14] SILBERMAN N, HOIEM D, KOHLI P, et al.Indoor segmentation and support inference from RGBD images[M]//Lecture Notes in Computer Science.Berlin, Heidelberg:Springer Berlin Heidelberg, 2012:746-760.
[15] FU Xueyang, ZHUANG Peixian, HUANG Yue, et al.A retinex-based enhancing approach for single underwater image[C]//Proceedings of 2014 IEEE International Conference on Image Processing (ICIP).Paris:IEEE, 2014.
[16] ANCUTI C, ANCUTI C O, HABER T, et al.Enhancing underwater images and videos by fusion[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition.June 16-21, 2012.Providence, RI.IEEE, 2012.
[17] DREWS P, DO NASCIMENTO E, MORAES F, et al.Transmission estimation in underwater single images[C]//Proceedings of 2013 IEEE International Conference on Computer Vision Workshops.Sydney:IEEE, 2013:825-830.
[18] GALDRAN A, PARDO D, PICÓN A, et al.Automatic Red-Channel underwater image restoration[J].Journal of Visual Communication and Image Representation, 2015, 26:132-145.
[19] YANG Miao, SOWMYA A.An underwater color image quality evaluation metric[J].IEEE Transactions on Image Processing, 2015, 24(12):6062-6071.
[20] PANETTA K, GAO Chen, AGAIAN S.Human-visual-system-inspired underwater image quality measures[J].IEEE Journal of Oceanic Engineering, 2016, 41(3):541-551.