激光与光电子学进展, 2018, 55 (8): 081009, 网络出版: 2018-08-13  

水面船只的成像处理与标定 下载: 551次

Imaging and Calibration of Ships on Water Surface
作者单位
江苏大学机械工程学院, 江苏 镇江 212000
摘要
针对水面最基本的晴天、夜晚和浓雾天气情况,设计了对应天气模式下船只图像的滤波处理以及对可疑船只进行定位的方案。对三种不同模式进行对应的高斯滤波、拉普拉斯图像增强和图像去雾处理,为船只定位提供了更好的图像质量。提取船只的轮廓特征并对轮廓点采用RDP(Ribosomal Database Project)算法进行特征点数的精简。根据提取的特征绘制船只位置并进行标定,提取中心点位置,完成定位。研究表明,夜晚模式下,图像灰度整体较低,本文方案对提高图像对比度有一定效果。Retinex算法对图像去雾提供了可行的处理方案。通过特征提取进行船只定位也有很好的实践效果。
Abstract
In view of the most basic sunny days, night and dense fog weather conditions, corresponding filtering of ship images and a scheme for positioning suspicious ships are designed. Gaussian filtering, Laplace image enhancement, and image dehazing are applied to the three different models, which provide better image quality for further ship positioning. The contour feature of the ship is extracted and the number of feature points is reduced by the RDP (Ribosomal Database Project) algorithm. The location of the ship is drawn and calibrated according to the feature extracted. The position of the central point can be extracted. The results show that the gray level of the image is low in the night mode, and the scheme proposed helps to improve the image contrast. The Retinex algorithm provides a feasible scheme for image dehazing. Feature extraction for ship positioning also has a good practical effect.
参考文献

[1] 王世刚, 游敏娟, 宋莉. 直方图均衡化图像增强的改进算法[J]. 中国医疗器械杂志, 2017, 41(3): 175-176.

    Wang S G, You M J, Song L. Improved algorithm of histogram equalization for image enhancement[J]. Chinese Journal of Medical Instrumentation, 2017, 41(3): 175-176.

[2] 李卫中, 易本顺, 邱康, 等. 细节保留的多曝光图像融合[J]. 光学 精密工程, 2016, 24(9): 2283-2292.

    Li W Z, Yi B S, Qiu K, et al. Detail preserving multi-exposure image fusion[J]. Optics and Precision Engineering, 2016, 24(9): 2283-2292.

[3] 常莉红, 冯象初, 张瑞. 四元数小波变换联合稀疏表示的图像融合[J]. 系统工程与电子技术, 2017, 39(7): 1633-1639.

    Chang L H, Feng X C, Zhang R. Image fusion scheme based on quaternion wavelet transform and sparse representation[J]. Systems Engineering and Electronics, 2017, 39(7): 1633-1639.

[4] Tan R T. Visibility in bad weather from a single image[C]∥IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2008: 1-8.

[5] He K, 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.

[6] 邵党国, 邓阳阳, 相艳, 等. 基于自适应高斯滤波的超声斑点降噪[J]. 数据采集与处理, 2017, 32(4): 746-753.

    Shao D G, Deng Y Y, Xiang Y, et al. Speckle reduction based on adaptive Gauss filtering[J]. Journal of Data Acquisition & Processing, 2017, 32(4): 746-753.

[7] 孙海江, 王延杰, 刘伟宁. 基于自适应平台阈值和拉普拉斯变换的红外图像增强[J]. 中国光学, 2011, 4(5): 474-479.

    Sun H J, Wang Y J, Liu W N. Enhancement of infrared images based on adaptive platform threshold and Laplace transformation[J]. Chinese Optics and Applied Optics, 2011, 4(5): 474-479.

[8] 贾海鹏, 张云泉, 龙国平, 等. 基于OpenGL的拉普拉斯图像增强算法优化的研究[J]. 计算机科学, 2012, 39(5): 271-277.

    Jia H P, Zhang Y Q, Long G P, et al. Research on Laplace image enhancement algorithm optimization based on OpenCL[J]. Computer Science, 2012, 39(5): 271-277.

[9] 王龙志, 姚晓天, 孟卓, 等. 基于自适应多尺度Retinex的光学相干层析图像衰减补偿算法[J]. 中国激光, 2013, 40(12): 1204001.

    Wang L Z, Yao X T, Meng Z, et al. An optical coherence tomography attenuation compensation algorithm based on adaptive multi-scale retinex[J]. Chinese Journal of Lasers, 2013, 40(12): 1204001.

[10] 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.

    Yang A P, Bai H H. Night time image defogging based on the theory of retinex and dark channel prior[J]. Laser & Optoelectronics Progress, 2017, 54(4): 041002.

[11] 李毅, 张云峰, 李宁, 等. 基于子带分解多尺度Retinex的红外图像自适应细节增强[J]. 中国激光, 2015, 42(5): 0512001.

    Li Y, Zhang Y F, Li N, et al. Adaptive detail enhancement for infrared image based on subband-decomposed multi-scale retinex[J]. Chinese Journal of Lasers, 2015, 42(5): 0512001.

[12] 江兴方. 遥感图像去云方法的研究及其应用[D]. 南京: 南京理工大学, 2007: 20-28.

    Jiang X F. Research and application of remoting cloud in sensing image[D]. Nanjing: Nanjing University of Science and Technology, 2007: 20-28.

[13] 李娟, 吴谨, 陈振学, 等. 基于自学习的稀疏正则化图像超分辨率方法[J]. 仪器仪表学报, 2015, 36(1): 194-200.

    Li J, Wu J, Chen Z X, et al. Self-learning image super-resolution method based on sparse representation[J]. Chinese Journal of Scientific Instrument, 2015, 36(1): 194-200.

[14] 鞠铭烨, 张登银. 基于先验知识与大气散射模型的图像增强算法[J]. 电子学报, 2017, 45(5): 1218-1225.

    Ju M Y, Zhang D Y. Image enhancement based on prior knowledge and atmospheric scattering model[J]. Acta Electronica Sinica, 2017, 45(5): 1218-1225.

[15] 刘杰平, 黄炳坤, 韦岗. 一种快速的单幅图像去雾算法[J]. 电子学报, 2017, 45(8): 1896-1901.

    Liu J P, Huang B K, Wei G. A fast effective single image dehazing algorithm[J]. Acta Electronica Sinica, 2017, 45(8): 1896-1901.

[16] 吴迪, 朱青松. 图像去雾的最新研究进展[J]. 自动化学报, 2015, 41(2): 221-239.

    Wu D, Zhu Q S. The latest research progress of image dehazing[J]. Acta Automatica Sinica, 2015, 41(2): 221-239.

[17] 李佳童, 章毓晋. 图像去雾算法的改进和主客观性能评价[J]. 光学 精密工程, 2017, 25(3): 735-741. DOI:10.3788/OPE.20172503.0735.[万方]

    Li J T, Zhang Y J. Improvements of image haze removal algorithm and its subjective and objective performance evaluation[J]. Optics and Precision Engineering, 2017, 25(3): 735-741.

[18] 孙红辉, 姚良, 张清华, 等. 基于神经网络的图像识别方法研究[J]. 光学技术, 2008, 34(S1): 173-174.

    Sun H H, Yao L, Zhang Q H, et al. Research on images identification method based on neural network[J]. Optical Technique, 2008, 34(S1): 173-174.

[19] 刘晓阳, 乔通, 乔智. 基于双边滤波和Retinex算法的矿井图像增强方法[J]. 工矿自动化, 2017, 43(2): 49-54.

    Liu X Y, Qiao T, Qiao Z. Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J]. Industry and Mine Automation, 2017, 43(2): 49-54.

[20] 方帅, 赵育坤, 李心科, 等. 基于光照估计的夜间图像去雾[J]. 电子学报, 2016, 44(11): 2569-2575.

    Fang S, Zhao Y K, Li X K, et al. Nighttime haze removal based on illumination estimation[J]. Acta Electronica Sinica, 2016, 44(11): 2569-2575.

[21] 王峰萍, 王卫星, 杨楠, 等. 基于改进Retinex的城市交通图像增强[J]. 交通运输系统工程与信息, 2017, 17(5): 53-59.

    Wang F P, Wang W X, Yang N, et al. An urban traffic image enhancement method based on modified retinex[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(5): 53-59.

姚红兵, 黄印, 卞锦文, 李航宇, 王成. 水面船只的成像处理与标定[J]. 激光与光电子学进展, 2018, 55(8): 081009. Yao Hongbing, Huang Yin, Bian Jinwen, Li Hangyu, Wang Cheng. Imaging and Calibration of Ships on Water Surface[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081009.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!