基于二维三次样条小波与红通道先验的水下图像增强方法
Underwater Image Enhancement Based on 2D Cubic Spline Wavelet and Red Channel Prior
DOI: 10.12677/JISP.2023.121006, PDF,    国家自然科学基金支持
作者: 刘 斌, 张 太：湖北大学计算机与信息工程学院，湖北 武汉
关键词: 水下图像；图像增强；不可分小波；红通道先验；白平衡；Underwater Images； Image Enhancement； Non-Separable Wavelet； Red Channel Priori； White Balance
摘要: 针对水下图像所出现的对比度低、细节模糊和颜色失真等问题，本文提出二维三次样条小波与暗通道先验的水下图像增强方法。将水下图像使用红通道先验方法去除雾状模糊，然后将其归一化以进行白平衡。同时将原始水下图像使用二维三次样条小波进行加性小波分解，产生低频子图和高频子图。将高频子图系数放大以增强细节信息。最后将处理后的高频子图和白平衡后的图像相加得到增强的图像。实验结果表明，本文算法能够有效消除图像颜色失真，增强的图像呈现出高对比度和清晰的细节。与目前水下图像增强相关典型方法相比，它在对比度、颜色、边缘保留和自然度等方面有明显改进。
Abstract: To address the problems of low contrast, blurred details and color distortion that occur in underwater images, this paper proposes a two-dimensional cubic spline wavelet with a dark channel prior for underwater image enhancement. The underwater image is removed from the fog blur using the red channel a priori method and then normalized for white balance. The original underwater image is also subjected to additive wavelet decomposition using two-dimensional cubic spline wavelets to produce low-frequency and high-frequency sub-images. The high-frequency sub-images coefficients are scaled up to enhance the detail information. Finally, the processed high-frequency sub-images and the white-balanced image are summed to obtain the enhanced image. The experimental results show that the algorithm in this paper can effectively eliminate the image color distortion and the enhanced image presents high contrast and clear details. It offers significant improvements in contrast, color, edge retention and naturalness compared to the typical methods related to current underwater image enhancement.
文章引用：刘斌, 张太. 基于二维三次样条小波与红通道先验的水下图像增强方法[J]. 图像与信号处理, 2023, 12(1): 51-60. https://doi.org/10.12677/JISP.2023.121006

参考文献

[1] 李黎, 王惠刚, 刘星. 基于改进暗原色先验和颜色校正的水下图像增强[J]. 光学学报, 2017, 37(12): 168-176.
[2] Liang, Z., Wang, Y., Ding, X., Mi, Z. and Fu, X. (2021) Single Underwater Image Enhancement by Attenuation Map Guided Color Correction and Detail Preserved Dehazing. Neurocomputing, 425, 160-172.
[Google Scholar] [CrossRef] 
[3] 郭继昌, 岳惠惠, 张怡, 刘迪, 刘晓雯, 郑司达. 图像增强对显著性目标检测的影响研究[J]. 中国图象图形学报, 2022, 27(7): 2129-2147.
[4] Arici, T., Dikbas, S. and Altunbasak, Y. (2009) A Histogram Modification Framework and Its Application for Image Contrast Enhancement. IEEE Transactions on Image Processing, 18, 1921-1935.
[Google Scholar] [CrossRef] 
[5] Chang, Y.C. and Chang, C.M. (2010) A Simple Histogram Modification Scheme for Contrast Enhancement. IEEE Transactions on Consumer Electronics, 56, 737-742.
[Google Scholar] [CrossRef] 
[6] 董丽丽, 丁畅, 许文海. 基于直方图均衡化图像增强的两种改进方法[J]. 电子学报, 2018, 46(10): 2367-2375.
[7] Chen, Y., Feng, P., Yang, J. and XIE, Z. (2012) An Adaptive Enhancement Algorithm of Low Illumine Color Video Image. International Journal of Sensing, Computing & Control, 2, 79-86.
[8] 仲伟峰, 袁东雪. 基于低照度的有雾彩色图像增强算法[J]. 激光与光电子学进展, 2020, 57(16): 223-229.
[9] 黄仁, 梁笑. 基于多尺度子带划分的雾霾天气图像增强方法[J]. 计算机应用, 2014, 34(S1): 215-218.
[10] 方帅, 杨静荣, 曹洋, 等. 图像引导滤波的局部多尺度Retinex算法[J]. 中国图象图形学报, 2012, 17(7): 748-755.
[11] Li, Q. and Liu, Q. (2015) Adaptive Enhancement Algorithm for Low Illumination Images Based on Wavelet Transform. Chinese Journal of Lasers, 42, 272-278.
[Google Scholar] [CrossRef] 
[12] Chiang, J.Y. and Chen, Y.C. (2011) Underwater Image Enhancement by Wavelength Compensation and Dehazing. IEEE Transactions on Image Processing, 21, 1756-1769.
[Google Scholar] [CrossRef] 
[13] Abd-Al Ameer, Z.S., Daway, H.G. and Kareem, H.H. (2019) Enhancement Underwater Image Using Histogram Equalization Based on. Journal of Engineering and Applied Sciences, 14, 641-647.
[Google Scholar] [CrossRef] 
[14] Sun, X., Liu, L., Li, Q., Dong, J., Lima, E. and Yin, R. (2019) Deep Pixel-to-Pixel Network for Underwater Image Enhancement and Restoration. IET Image Processing, 13, 469-474.
[Google Scholar] [CrossRef] 
[15] Patil, P., Patil, C., Patil, U. and Dmello, S. (2019) Enhancing Underwater Image by Fusion. International Journal of Computer Science and Mobile Computing, 8, 69-72.
[16] Islam, M. J., Xia, Y. and Sattar, J. (2020) Fast Underwater Image Enhancement for Improved Visual Perception. IEEE Robotics and Automation Letters, 5, 3227-3234.
[Google Scholar] [CrossRef] 
[17] Zhang, W., Li, G. and Ying, Z. (2018) Underwater Image Enhancement by the Combination of Dehazing and Color Correction. Pacific Rim Conference on Multimedia, Hefei, 21-22 September 2018, 145-155.
[Google Scholar] [CrossRef] 
[18] Mi, Z., Liang, Z., Wang, Y., Fu, X. and Chen, Z. (2018) Multi-Scale Gradient Domain Underwater Image Enhancement. 2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), Kobe, 28-31 May 2018, 1-5.
[Google Scholar] [CrossRef] 
[19] Moni, J. and Prakash, A.J. (2018) Color Balance and Fusion for Underwater Image Enhancement: Survey. International Research Journal of Engineering and Technology (IRJET), 5, 1391-1394.
[20] Cheng, N., Zhao, T., Chen, Z. and Fu, X. (2018) Enhancement of Underwater Images by Super-Resolution Generative Adversarial Networks. Proceedings of the 10th International Conference on Internet Multimedia Computing and Service, Nanjing, 17-19 August 2018, 1-4.
[Google Scholar] [CrossRef] 
[21] Hou, M., Liu, R., Fan, X. and Luo, Z. (2018) Joint Residual Learning for Underwater Image Enhancement. 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, 7-10 October 2018, 4043-4047.
[Google Scholar] [CrossRef] 
[22] AncutiT, C. O., Ancuti, C. andBaltaT, H. (2018) Effective Local Backscattering Estimation for Underwater Image Enhancement. OCEANS 2018 MTS/IEEE Charleston, Charleston, 22-25 October 2018, 1-4.
[Google Scholar] [CrossRef] 
[23] Deperlioglu, O. and Kose, U. (2018) Practical Method for the Underwater Image Enhancement with Adjusted CLAHE. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, 28-30 September 2018, 1-6.
[Google Scholar] [CrossRef] 
[24] Mathur, M. and Goel, N. (2018) Enhancement of Underwater Images Using White Balancing and Rayleigh-Stretching. 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 22-23 February 2018, 924-929.
[Google Scholar] [CrossRef] 
[25] Chen, Y.K., Miaou, S.G., Lin, Y.C. and Chen, Y.M. (2018) Color Image Enhancement Using Luminance Histogram Equalization and Two-Factor Saturation Control. 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, 25-29 June 2018, 159-164.
[Google Scholar] [CrossRef] 
[26] Nunez, J., Otazu, X., Fors, O., Prades, A., Pala, V. and Arbiol, R. (1999) Multiresolution-Based Image Fusion with Additive Wavelet Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 37, 1204-1211.
[Google Scholar] [CrossRef] 
[27] Jaffe, J.S. (1990) Computer Modeling and the Design of Optimal Underwater Imaging Systems. IEEE Journal of Oceanic Engineering, 15, 101-111.
[Google Scholar] [CrossRef] 
[28] Ketcham, D.J. (1976) Real-Time Image Enhancement Techniques. Proceedings of SPIE-OSA, 74, 120-125.
[Google Scholar] [CrossRef] 
[29] He, K., Sun, J. and Tang, X. (2010) Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 2341-2353.
[Google Scholar] [CrossRef] 
[30] Galdran, A., Pardo, D., Picón, A. and Alvarez-Gila, A. (2015) Automatic Red-Channel Underwater Image Restoration. Journal of Visual Communication and Image Representation, 26, 132-145.
[Google Scholar] [CrossRef] 
[31] Song, W., Wang, Y., Huang, D., Liotta, A. and Perra, C. (2020) Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map. IEEE Transactions on Broadcasting, 66, 153-169.
[Google Scholar] [CrossRef] 
[32] Wang, S., Ma, K., Yeganeh, H., Wang, Z. and Lin, W. (2015) A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images. IEEE Signal Processing Letters, 22, 2387-2390.
[Google Scholar] [CrossRef] 
[33] Treeby, B.E., Varslot, T.K., Zhang, E.Z., Laufer, J.G. and Beard, P.C. (2011) Automatic Sound Speed Selection in Photoacoustic Image Reconstruction Using an Autofocus Approach. Journal of Biomedical Optics, 16, Article ID: 090501.
[Google Scholar] [CrossRef] [PubMed]