期刊文献+

基于内河单幅图像的去雾算法研究 被引量:2

A Haze Removal Algorithm of Single Inland Image
下载PDF
导出
摘要 图像景深和天空亮度值的求取是图像复原方法去雾的核心问题,但目前的去雾算法都是基于一定的假设条件来求取这2个值的,对于色彩单调、天空区域较大的内河图像去雾效果不理想.通过对内河航道视频图像的研究,提出将直方图多峰均值法和位平面分解法相结合的方式来求得天空亮度值并分割天空区域;采用分区域的景深计算方法求得图像任意一点的景深值.然后基于大气散射模型,完成内河雾天单幅图像的去雾处理.为了客观评价去雾后图像的质量,从图像的可见边数目比、平均梯度比和图像熵值三方面进行了去雾效果的比较.实验证明,该算法对内河航道图像有良好的去雾效果。 Obtaining scene depth and the sky brightness value is the core issue for fog image restoration method . However ,the present haze removal algorithms are all based on certain assumptions to obtain these two parameters and are not effective enough for processing drab and large sky area inland images .By studying the inland waterway video image , this paper combines the histogram multimodal averaging and bit plane decomposition method to get the sky brightness val -ue to split the sky area .Then ,by using the separate area scene depth calculation method ,the image scene depth of any point is obtained .Finally ,according to the atmospheric scattering model ,the haze removal of single inland image is com-pleted .In order to evaluate the proposed methods ,the paper compares the haze removal effectiveness from three aspects including the visible image edge number ratio ,average gradient ratio and image entropy value .Experiments show that the proposed algorithm is effective for the fog inland image .
出处 《交通信息与安全》 2014年第1期84-90,共7页 Journal of Transport Information and Safety
基金 国家自然科学基金项目(批准号:51279152)资助
关键词 内河 直方图多峰均值 景深 去雾 质量评价 inland histogram multimodal average scene depth haze removal quality evaluation
  • 相关文献

参考文献18

  • 1Oakley J P, Satherley B L. Improving image quality in poor visibility conditions using a physical model for contrast degradation[J]. IEEE Transactions on Image Processing,1998,7(2) :167-170.
  • 2Narasimhan S G, Nayar S K. Removing weat-her effects from monochrome images Proce-edings of CVPR[C]//Kauai, Hawaii: IEEE Computer Socie- ty, 2001:186-193.
  • 3Narasimhan S G, Nayar S K. Chromatic fram-ework for vision in bad weather Proeeedin-gsof IEEE CVPR [C]//SOuth Carolina: IEEE Co-mputer Society, 2000: 598-605.
  • 4Schechner Y Y, Narasimhan S G, Nayar S K. In- stant dehazing of images usingpolarization[C]//Pro- ceedings of IEEE CVPR. Kauai, Hawaii: IEEE Computer Society, 2001: 325-332.
  • 5Kopf J, Neubert B, Chen B, et al. Deep pho-to: Model-based photograph enhancement and viewing [J]. Special Interest Group for Computer GRAPH ICS, 2008,27(5) :11-15.
  • 6He K M, Sun J, Zhou X O. Single image haze re- moval using dark channelprior[C] // Proceedings of IEEE CVPR. Miami, FL: IEEE Computer Society, 2009:1956- 1963.
  • 7GuoJ, WangXT, Hu C P, et al. Simple De fog- ging Method For Outdoor Images Based On Physical Model[C] // Proceedings of 5th International Sym posium on Advanced Optical Manufacturing and Testing Technologies. Dalian: SPIE, 2010, 7658: 76580M-1-76580M 6.
  • 8郭珈,王孝通,胡程鹏,徐晓刚.基于单幅图像景深和大气散射模型的去雾方法[J].中国图象图形学报,2012,17(1):27-32. 被引量:20
  • 9祝培,朱虹,钱学明,李晗.一种有雾天气图像景物影像的清晰化方法[J].中国图象图形学报(A辑),2004,9(1):124-128. 被引量:114
  • 10黄明晶,刘清,熊燕帆,郭建明.面向内河雾天图像的大气光亮度值估算方法研究[J].交通信息与安全,2013,31(3):33-38. 被引量:4

二级参考文献44

  • 1王欣威,李颖,董慧颖,陈海波.基于大气模型的天气退化图像复原方法及应用[J].沈阳理工大学学报,2005,24(1):32-35. 被引量:4
  • 2王志坚.基于大气模型的图像复原改进算法及应用[J].计算机工程与应用,2007,43(3):239-241. 被引量:8
  • 3Oakley John P, Satherley Brenda L.Improvlng image quality in poor visibility conditions using a physical model for contrast degrsdation [J]. IEEE Transactions on Image Processing, 1998,7(2):167-179.
  • 4Narasimhan Srinivasa G, Nayar Shree K. Removing weather effects from monochrome images [A]. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Reeognition[C], Kauai, Hawaii, USA, 2001,186 -193.
  • 5Kim Tae Keun, Paik Joon Ki, Kang Bong Soon. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering [J]. IEEE Transactions on Consumer Electronics, 1998,44(1): 82-86.
  • 6Kim Joung-Youn, Kim Lee-Sup, Hwang Seung-Ho. An advaneed eontrast enhaneement using partially overlapped subblock histogram equalization[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001,11 (4) :475-484.
  • 7Pinson M H, Wolf S. A new standardized method for objectively measuring video quality[J]. IEEE Transactions on Broadcasting, 2004, 50(3):312-322.
  • 8Sugimoto O, Kawada R, Koike A. Development of picture quality monitoring system for IPTV service based on the reduced reference framework[C]//In Proceedings of SPIE, San Jose, 2006:1-9.
  • 9Callet L P, Gaudin V C, Barba D. A convolutional neural network approach for objective video quality assessment [J]. IEEE Transactions on Neural Networks, 2006, 17(5): 1 316-1 327.
  • 10Callet L P, Gaudin V C. No reference and reduced reference video quality metrics for end to end QoS monitoring [J]. IEICE Transactions on Communications, 2006(2): 289-296.

共引文献137

同被引文献15

引证文献2

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部