首页 > 论文 > 激光与光电子学进展 > 55卷 > 2期(pp:21009--1)

基于红色暗通道先验和逆滤波的水下图像复原

Underwater Image Restoration Based on Red-Dark Channel Prior and Inverse Filtering

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

为提升水下图像的视觉效果, 提出了基于红色暗通道先验(RDCP)和逆滤波的水下图像复原算法。该算法首先简化Jaffe-McGlamery水下光学成像模型, 在此基础上, 利用RDCP消除水下成像过程中后向散射引起的图像雾化效果;然后结合各通道透射率图与光学传递函数的数学关系, 采用逆滤波去除前向散射分量;最后采用基于高斯分布的线性拉伸提高图像对比度。使用该算法与几种主流的水下图像处理算法对多种水下环境拍摄得到的图像进行处理, 并计算信息熵等客观评价指标。实验结果表明, 该算法能够更好地平衡图像的色度、对比度及饱和度, 视觉效果更接近自然场景下的图像。

Abstract

In order to improve the visual effect of underwater images, an underwater image restoration algorithm based on red-dark channel prior (RDCP) and inverse filtering is proposed. Firstly, the Jaffe-McGlamery underwater optical imaging model is simplified. On this basis, the RDCP is used to eliminate the foggy appearance of images resulting from backward scattering during the imaging process. Secondly, considering the mathematic relation between the transmission map of each channel and optical transfer function, inverse filtering is applied to remove the forward scattering component. Finally, the proposed algorithm adopts linear stretch based on Gaussian distribution to improve image contrast. The proposed algorithm and several main underwater image processing algorithms are employed in processing underwater images captured in various underwater environments, and the information entropy and other objective evaluation factors are calculated. The experimental results prove that the proposed algorithm has superiority for balancing the color, contrast and saturation of the images, and the visual effects are more similar to images captured in natural settings.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TN911.73

DOI:10.3788/lop55.021009

所属栏目:图像处理

基金项目:国家自然科学基金(61372145)、天津大学独立创新基金(2015XZC-0005)

收稿日期:2017-08-21

修改稿日期:2017-09-15

网络出版日期:--

作者单位    点击查看

徐岩:天津大学电气自动化与信息工程学院, 天津 300072
曾祥波:天津大学电气自动化与信息工程学院, 天津 300072

联系人作者:曾祥波(xiaozeng@tju.edu.cn)

备注:徐岩(1977—), 女, 博士, 副教授, 硕士生导师, 主要从事数字视频信号处理方面的研究。E-mail: xuyan@tju.edu.cn

【1】Jaffe J S. Computer modeling and the design of optimal underwater imaging systems[J]. Journal of Oceanic Engineering, 1990, 15(2): 101-111.

【2】Zhang K, Jin W Q, Qiu S, et al. Multi-scale Retinex enhancement algorithm on luminance channel of color underwater image[J]. Infrared Technology, 2011, 33(11): 630-634.
张凯, 金伟其, 裘溯, 等. 水下彩色图像的亮度通道多尺度Retinex增强算法[J]. 红外技术, 2011, 33(11): 630-634.

【3】Fu X Y, Zhuang P X, Huang Y, et al. A retinex-based enhancing approach for single underwater image[C]. IEEE International Conference on Image Processing, 2014: 4572-4576.

【4】Wen H C, Tian Y H, Huang T J, et al. Single underwater image enhancement with a new optical model[C]. IEEE International Symposium on Circuits and Systems, 2013: 753-756.

【5】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.

【6】Ancuti C, Ancuti C O, Haber T, et al. Enhancing underwater images and videos by fusion[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 81-88.

【7】Ni J Y, Li Q W, Zhou Y Q, et al. Underwater image restoration based on transmittance optimization and color temperature adjustment[J]. Laser & Optoelectronics Progress, 2017, 54(1): 011001.
倪锦艳, 李庆武, 周亚琴, 等. 基于透射率优化和色温调节的水下图像复原[J]. 激光与光电子学进展, 2017, 54(1): 011001.

【8】Yang A P, Zheng J, Wang J, et al. Underwater image restoration based on color cast removal and dark channel prior[J].Journal of Electronics & Information Technology, 2015, 37(11): 2541-2547
杨爱萍, 郑佳, 王建, 等. 基于颜色失真去除与暗通道先验的水下图像复原[J]. 电子与信息学报, 2015, 37(11): 2541-2547.

【9】Shen Y, Dang J W, Wang Y P, et al. A color underwater image clearness algorithm based on Tetrolet transform[J]. Acta Optica Sinica, 2017, 37(9): 0910002.
沈瑜, 党建武, 王阳萍, 等. 基于Tetrolet变换的彩色水下图像清晰化算法[J]. 光学学报, 2017, 37(9): 0910002.

【10】He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.

【11】Gao Y, Yun L J, Shi J S, et al. Enhancement dark channel algorithm of fog image based on the TV model[J]. Chinese Journal of Lasers, 2015, 42(8): 0809001.
高银, 云利军, 石俊生, 等. 基于TV模型的暗原色理论雾天图像复原算法[J]. 中国激光, 2015, 42(8): 0809001.

【12】Yang A P, Bai H H. Nighttime image defogging based on the theory of retinex and dark channel prior[J]. Laser & Optoelectronics Progress, 2017, 54(4): 041002.
杨爱萍, 白煌煌. 基于Retinex理论和暗通道先验的夜间图像去雾算法[J]. 激光与光电子学进展, 2017, 54(4): 041002.

【13】Wang Y, Wu B. Fastclear single underwater image[C]. Conference on Computational Intelligence and Software Engineering, 2010: 1706794.

【14】Zhao X W, Jin T, Qu S. Deriving inherent optical properties from background color and underwater image enhancement[J]. Ocean Engineering, 2015, 94: 163-172.

【15】Gould R W, Arnone R A, Martinolich P M. Spectral dependence of the scattering coefficient in case 1 and case 2 waters[J]. Applied Optics, 1999, 38(12): 2377-2383.

【16】Li C Y, Guo J C, Cong R M, et al. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior[J]. IEEE Transactions on Image Processing, 2016, 25(12): 5664-5677.

【17】Trucco E, Olmos-Antillon A T. Self-tuning underwater image restoration[J]. Journal of Oceanic Engineering, 2006, 31(2): 511-519.

【18】Fang Y M, Ma K D, Wang Z, et al. No-reference quality assessment of contrast-distorted images based on natural scene statistics[J]. IEEE Signal Processing Letters, 2014, 22(7): 838-842.

【19】Yang M, Sowmya A.An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015, 24(12): 6062-6071.

引用该论文

Xu Yan,Zeng Xiangbo. Underwater Image Restoration Based on Red-Dark Channel Prior and Inverse Filtering[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021009

徐岩,曾祥波. 基于红色暗通道先验和逆滤波的水下图像复原[J]. 激光与光电子学进展, 2018, 55(2): 021009

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF