基于MSR的水下图像增强算法研究
Underwater Image Enhancement Algorithm Based on Improved Retinex Method
DOI: 10.12677/CSA.2018.81002, PDF,  被引量    科研立项经费支持
作者: 李社蕾*, 辛光红:三亚学院,信息与智能工程学院,海南 三亚;李海涛:92961部队,海南 三亚
关键词: 水下图像颜色校正图像增强直方图均衡RetinexUnderwater Image Color Correction Image Enhancement Histogram Equalization Retinex
摘要: 针对在水下环境中,由于水体对光的吸收和光的散射,以及深海图像由于人工光源光线不均匀,使得水下图像存在颜色失真、图像模糊及曝光不足等问题。根据先消除图像模糊后颜色校正的思路,本文提出了一种多尺度Retinex算法和基于颜色通道直方图量化的颜色校正算法相结合的水下图像处理新算法,该方法采用了基于中心环绕的多尺度Retinex算法(Multi-Scale Retinex, MSR)。并采用基于颜色通道直方图量化的颜色校正算法调整图像,以改善水下图像对比度低、颜色衰减、整体模糊、细节不清晰等问题。实验结果表明,论文提出的方法有效地降低了色彩失真的影响,又较好地突出了图像细节,使水下彩色图像具有更好的视觉效果。
Abstract: The paper aimed at improving the problem of color distortion, blur and underexposure, which caused by absorption of water and light scattering in the underwater environment, and uneven light caused by artificial illumination in the deep sea. According to the ideal of removing the color distortion after the image blurring removal, a novel method of image enhancement has propose, combining multi-scale retinex (MSR) and color correction algorithm based on color histogram quantization. The color correction algorithm based on color channel histogram was used to adjust the image to improve problems of the low contrast, color attenuation, overall fuzzy, unclear details and so on. The experimental results showed that the proposed method can effectively reduce the influence of color distortion and highlight the image details, so that the underwater color image has better visual effect.
文章引用:李社蕾, 李海涛, 辛光红. 基于MSR的水下图像增强算法研究[J]. 计算机科学与应用, 2018, 8(1): 9-15. https://doi.org/10.12677/CSA.2018.81002

参考文献

[1] 徐岩, 马硕, 王权威. 一种利用前景模型的水下图像增强算法[J]. 小型微型计算机系统, 2017, 38(12): 2802-2806.
[2] Land, E.H. and Mc Cann, J.J. (1971) Lightness and Retinex Theory. Journal of the Optical Society of America, 61, 1- 11. [Google Scholar] [CrossRef
[3] Zhang, S., Wang, T., Dong, J.Y. and Yu, H. (2017) Underwater Image Enhancement via Extended Multi-Scale Retinex. Neurocomputing, 245, 1-9. [Google Scholar] [CrossRef
[4] Wang, Y., Wang, H., Yin, C. and Dai, M. (2016) Biologically Inspired Image Enhancement Based on Retinex. Neurocomputing, 177, 373-384. [Google Scholar] [CrossRef
[5] Jung, C., Sun, T. and Jiao, L. (2013) Eye Detection under Varying Illumination Using the Retinex Theory. Neurocomputing, 113, 130-137. [Google Scholar] [CrossRef
[6] Joshi, K.R. and Kamathe, R.S. (2008) Quantification of Retinex in Enhancement of Weather Degraded Images. International Conference on Audio, Language and Image Processing (ICALIP 2008), July 7-9 2008, Shanghai, 1229-1233. [Google Scholar] [CrossRef
[7] SM, A.R. and Supriya, M. (2015) Underwater Image Enhancement Using Single Scale Retinex on a Reconfigurable Hardware. 2015 IEEE International Symposium on Ocean Electronics (SYMPOL), 1-5.
[8] Fu, X.Y., Zhuang, P.X. and Liao, Y.H. (2014) A Retinex-Based Enhancement Approach for Single Underwater Image. Proceedings of IEEE International Conference on Image Processing, October 27-30 2014, Paris, 4572-4576.
[9] 曾凡锋, 刘树鹏. Retinex在光照不均文本图像中的研究[J]. 计算机工程与设计, 2017, 38(11): 3072-3079.
[10] Rahman, Z., Jobson, D.J. and Woodell, G.A. (1996) Multi Scale Retinex for Color Image Enhancement. International Conferences on Proceedings of Image Processing, September 19-19 1996, Lausanne, 1103-1106. [Google Scholar] [CrossRef
[11] Yu, T. and Wang, R. (2016) Scene Parsing Using Graph Matching on Street-View Data. Computer Vision and Image Understanding, 145, 70-80. [Google Scholar] [CrossRef
[12] Tan, M., Hu, Z., Wang, B., Zhao, J. and Wang, Y. (2016) Robust Object Recognition via Weakly Supervised Metric and Template Learning. Neurocomputing, 181, 96-107. [Google Scholar] [CrossRef