图像去雾方法和评价及其应用研究
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摘要
雾天降质图像的去雾处理是计算机视觉领域中的重要问题与研究热点,具有广阔的应用前景。但是由于导致雾天图像降质的原因复杂,且雾天图像本身的信息量不多,现有的算法和退化模型在描述图像降质的根本原因和应用于特定场景中时均存在不尽如人意之处,视觉改善的效果仍有较大的提升空间。因此,在分析雾天降质图像退化机理的基础上,研究如何对其进行有效的清晰化处理具有重要的理论和实际意义。
自动去雾技术研究主要涉及四个问题：单幅图像去雾处理、视频去雾处理、构建去雾效果的客观评价体系和去雾技术在实际中的应用。本文针对这四个方面展开深入的研究,探索了去雾技术的新理论、新方法,并为改善视觉系统在恶劣天气条件下的工作性能奠定一定的理论基础。本文具体完成的工作如下：
针对已有基于暗原色原理的去雾算法具有时空复杂度高的问题,而基于大气耗散函数的去雾方法则存在算法参数多且需人工调整等问题,提出了一种基于传播图梯度优先规律的去雾方法。该方法依据传播图梯度优先规律,通过对大气耗散函数图像进行快速双边滤波来获取传播图中的少量精细化像素,而对于最终传播图中的大量像素则直接来自由暗原色原理所得到的粗略传播图。该方法不仅显著减小了计算量,而且避开了复杂矩阵项的求解和多参数的设置。在对大量有雾图像的清晰化实验中,该方法获得了较好的结果。
依据雾天图像所具有的频谱特性,提出了一种基于傅里叶振幅谱特性雾天检测方法,并通过实验验证了该方法的有效性。针对已有视频去雾算法存在块状效应等问题,本文提出了两种基于雾气理论的视频去雾算法,一种视雾气为覆盖在各帧图像上的一层遮罩被从原视频中减除,一种视雾气为光路传播图被分离消除。前者将Retinex算法得到的亮度图像与视频帧自身的深度关系相结合求取雾气遮罩,并将此遮罩从原视频帧中分离以去除雾气；后者将由背景图像得到的视频“通用”传播图应用于视频的所有帧以消除雾气。实验证明两种算法均能有效地提高原有雾视频各帧的清晰度。所提算法从雾气的角度入手,无需借助参考图像,运算代价低,与一般视频去雾算法相比,在获得较优的去雾效果的同时,具有较好的实用性和较快的处理速度。
提出建立去雾效果综合评价体系。在分析指出已有对比度增强评估方法不足之处的基础上,提出对于能检测出能见度的场景,将去雾前后能见度数值的提升作为一项最重要的评价指标,而对于无法检测能见度的场景,则分别提出了两种图像复原效果评价方法。一种借助由环境渲染或光路传播图所模拟的雾环境图像,采用全参考方式评估算法的去雾效果；一种从人类视觉感知的角度出发,构建综合评价体系以全面衡量算法的去雾性能。实验证明两种方法均能有效地评价各去雾算法的复原效果,且评估结果与人眼主观感受相一致。所提评价方法分别从计算能见度、构建模拟雾环境和人类视觉感知三方面考虑,与已有评价方法相比,在获得全方面评估结论的同时,具有较好的实用性和可靠性。
在分析总结交通场景图像相关特征的基础上,依据这些特征提出了一种专门针对交通场景的去雾算法。该算法引入能见度的思想,采用对图像近处区域弱增强,对驾驶员所感兴趣的远处区域重点增强的方式,以区别于已有去雾方法对整幅图像进行统一增强的方式,从而实现了对雾天交通场景图像更为有效的清晰化处理。同时,围绕交通环境下的相关典型应用,即雾天环境下的道路车道线特征提取、交通标志牌检测和交警手势识别三个方面,分别对所提去雾算法的清晰化性能展开研究,通过实验验证了该去雾算法在这些应用场景下的有效性和实用性。
Fog removal for degraded image is a fundamental and hot problem in computer vision, which has a wide applicate prospect. However, the reasons for foggy image degradation are very complicated and the information in foggy image itself is insufficient. Currently, the degraded process of a foggy image can not be described by any algorithms and models perfectly, and the previous algorithms are not good enough when using them in particular scene. There is still much works to improve the visual effect. Thus, it is necessary to research the fog-degraded image clearness techniques based on the analysis of image degradation mechanism.
There are four sub-problems involved in automatic defogging technique:fog removal for single image, fog removal for video, objective assessment of defogging effect for color image and the application of defogging technique. Dedicated to the four sub-problems the thesis developed a deep research, explore the new theory and method for defogging technique, and laid a theoretical foundation for improving the performance of vision system in bad weather condition. The main works of the thesis in research are shown as following:
Due to the high time-temporal complexity of the defogging algorithm based on dark channel prior, and the user interaction of the defogging algorithm based on atmospheric veil. A fog removal algorithm based on the transmission gradient prior is proposed in this thesis. According to the prior, only small amount of pixels need to be refined by using fast bilateral filter on the atmospheric veil, while most pixels in transmission map can be directly estimated by using dark channel prior. The proposed algorithm not only obviously reduces the computation cost, but also has no complex matrix and too many parameters. The method has been tested using a lot of foggy images, and is found to give a good results.
According to the frequency spectrum of the foggy image, a fog detection method based on Fourier spectrum of the entire image is proposed. Several experiments demonstrate the effectiveness of the proposed method. Aims to the block effectiveness problem of the previous video defogging algorithm, two new video defogging algorithms based on fog theory are proposed in the thesis. One is regarding fog as the veil layer to be subtracted, and the other is taking fog as the transmission map to be separated from the original video. The former uses the luminance component image obtained by Retinex algorithm and the depth information of the original video frames to separate the veil layer. The latter applies a single transmission map obtained from the background image to a series of video frames. Experiments show that both algorithms can effectively improve the quality of the video frames. Compared with other algorithms, our algorithms restore video frames from a perspective of fog with no reference image and low computation cost. The new algorithms can remove fog effectively as well as provide a good practicability and a fast speed.
A framework for defogging effect assessment is constructed in the paper. By analyzing the limitation of the previous assessment method based on contrast enhancement. We propose that for the scene where the visibility can be detected, the visibility increased-value through fog and fog removal images is compted as an important assessment index. While for the scene where the visibility can't be detected, two new assessment methods for the clearness effect of image defogging algorithm are proposed in the thesis. One is full-reference method based on the synthetic foggy images which are obtained by environment rendering or transmission map, and the other is developing comprehensive assessment system to assess defogging effct from human vision perception. Experiment show that both methods can assess the effect effectively, and the assessment results are consistent with our subjective perception. Compared with other existing methods, our proposed methods assess defogging effect from the visibility computation, synthetic foggy image and human visual perception, respectively. The new methods can obtain an overall conclusion as well as provide a good practicability and reliability.
By analyzing the feature of foggy traffic image, a defogging algorithm, targets those traffic scene is presented by introducing the visibility concept into the defogging process. The algorithm can obtain a better defogging effect by limiting the possibilities of enhancement at short distance, while strongly enhancing the long distance area that is important for drivers, which is different from the unified enhancement of the previous defogging algorithms. Meanwhile, we propose some applications of the proposed defogging algorithm:improvement of road marking features extraction, improvement of circular road signs detection and improvement of traffic police gesture recognition. Experiments performed in those application scenes demonstrate the effectiveness and the practicality of the proposed algorithm.
自动去雾技术研究主要涉及四个问题：单幅图像去雾处理、视频去雾处理、构建去雾效果的客观评价体系和去雾技术在实际中的应用。本文针对这四个方面展开深入的研究,探索了去雾技术的新理论、新方法,并为改善视觉系统在恶劣天气条件下的工作性能奠定一定的理论基础。本文具体完成的工作如下：
针对已有基于暗原色原理的去雾算法具有时空复杂度高的问题,而基于大气耗散函数的去雾方法则存在算法参数多且需人工调整等问题,提出了一种基于传播图梯度优先规律的去雾方法。该方法依据传播图梯度优先规律,通过对大气耗散函数图像进行快速双边滤波来获取传播图中的少量精细化像素,而对于最终传播图中的大量像素则直接来自由暗原色原理所得到的粗略传播图。该方法不仅显著减小了计算量,而且避开了复杂矩阵项的求解和多参数的设置。在对大量有雾图像的清晰化实验中,该方法获得了较好的结果。
依据雾天图像所具有的频谱特性,提出了一种基于傅里叶振幅谱特性雾天检测方法,并通过实验验证了该方法的有效性。针对已有视频去雾算法存在块状效应等问题,本文提出了两种基于雾气理论的视频去雾算法,一种视雾气为覆盖在各帧图像上的一层遮罩被从原视频中减除,一种视雾气为光路传播图被分离消除。前者将Retinex算法得到的亮度图像与视频帧自身的深度关系相结合求取雾气遮罩,并将此遮罩从原视频帧中分离以去除雾气；后者将由背景图像得到的视频“通用”传播图应用于视频的所有帧以消除雾气。实验证明两种算法均能有效地提高原有雾视频各帧的清晰度。所提算法从雾气的角度入手,无需借助参考图像,运算代价低,与一般视频去雾算法相比,在获得较优的去雾效果的同时,具有较好的实用性和较快的处理速度。
提出建立去雾效果综合评价体系。在分析指出已有对比度增强评估方法不足之处的基础上,提出对于能检测出能见度的场景,将去雾前后能见度数值的提升作为一项最重要的评价指标,而对于无法检测能见度的场景,则分别提出了两种图像复原效果评价方法。一种借助由环境渲染或光路传播图所模拟的雾环境图像,采用全参考方式评估算法的去雾效果；一种从人类视觉感知的角度出发,构建综合评价体系以全面衡量算法的去雾性能。实验证明两种方法均能有效地评价各去雾算法的复原效果,且评估结果与人眼主观感受相一致。所提评价方法分别从计算能见度、构建模拟雾环境和人类视觉感知三方面考虑,与已有评价方法相比,在获得全方面评估结论的同时,具有较好的实用性和可靠性。
在分析总结交通场景图像相关特征的基础上,依据这些特征提出了一种专门针对交通场景的去雾算法。该算法引入能见度的思想,采用对图像近处区域弱增强,对驾驶员所感兴趣的远处区域重点增强的方式,以区别于已有去雾方法对整幅图像进行统一增强的方式,从而实现了对雾天交通场景图像更为有效的清晰化处理。同时,围绕交通环境下的相关典型应用,即雾天环境下的道路车道线特征提取、交通标志牌检测和交警手势识别三个方面,分别对所提去雾算法的清晰化性能展开研究,通过实验验证了该去雾算法在这些应用场景下的有效性和实用性。
Fog removal for degraded image is a fundamental and hot problem in computer vision, which has a wide applicate prospect. However, the reasons for foggy image degradation are very complicated and the information in foggy image itself is insufficient. Currently, the degraded process of a foggy image can not be described by any algorithms and models perfectly, and the previous algorithms are not good enough when using them in particular scene. There is still much works to improve the visual effect. Thus, it is necessary to research the fog-degraded image clearness techniques based on the analysis of image degradation mechanism.
There are four sub-problems involved in automatic defogging technique:fog removal for single image, fog removal for video, objective assessment of defogging effect for color image and the application of defogging technique. Dedicated to the four sub-problems the thesis developed a deep research, explore the new theory and method for defogging technique, and laid a theoretical foundation for improving the performance of vision system in bad weather condition. The main works of the thesis in research are shown as following:
Due to the high time-temporal complexity of the defogging algorithm based on dark channel prior, and the user interaction of the defogging algorithm based on atmospheric veil. A fog removal algorithm based on the transmission gradient prior is proposed in this thesis. According to the prior, only small amount of pixels need to be refined by using fast bilateral filter on the atmospheric veil, while most pixels in transmission map can be directly estimated by using dark channel prior. The proposed algorithm not only obviously reduces the computation cost, but also has no complex matrix and too many parameters. The method has been tested using a lot of foggy images, and is found to give a good results.
According to the frequency spectrum of the foggy image, a fog detection method based on Fourier spectrum of the entire image is proposed. Several experiments demonstrate the effectiveness of the proposed method. Aims to the block effectiveness problem of the previous video defogging algorithm, two new video defogging algorithms based on fog theory are proposed in the thesis. One is regarding fog as the veil layer to be subtracted, and the other is taking fog as the transmission map to be separated from the original video. The former uses the luminance component image obtained by Retinex algorithm and the depth information of the original video frames to separate the veil layer. The latter applies a single transmission map obtained from the background image to a series of video frames. Experiments show that both algorithms can effectively improve the quality of the video frames. Compared with other algorithms, our algorithms restore video frames from a perspective of fog with no reference image and low computation cost. The new algorithms can remove fog effectively as well as provide a good practicability and a fast speed.
A framework for defogging effect assessment is constructed in the paper. By analyzing the limitation of the previous assessment method based on contrast enhancement. We propose that for the scene where the visibility can be detected, the visibility increased-value through fog and fog removal images is compted as an important assessment index. While for the scene where the visibility can't be detected, two new assessment methods for the clearness effect of image defogging algorithm are proposed in the thesis. One is full-reference method based on the synthetic foggy images which are obtained by environment rendering or transmission map, and the other is developing comprehensive assessment system to assess defogging effct from human vision perception. Experiment show that both methods can assess the effect effectively, and the assessment results are consistent with our subjective perception. Compared with other existing methods, our proposed methods assess defogging effect from the visibility computation, synthetic foggy image and human visual perception, respectively. The new methods can obtain an overall conclusion as well as provide a good practicability and reliability.
By analyzing the feature of foggy traffic image, a defogging algorithm, targets those traffic scene is presented by introducing the visibility concept into the defogging process. The algorithm can obtain a better defogging effect by limiting the possibilities of enhancement at short distance, while strongly enhancing the long distance area that is important for drivers, which is different from the unified enhancement of the previous defogging algorithms. Meanwhile, we propose some applications of the proposed defogging algorithm:improvement of road marking features extraction, improvement of circular road signs detection and improvement of traffic police gesture recognition. Experiments performed in those application scenes demonstrate the effectiveness and the practicality of the proposed algorithm.
引文
[l]郭璠,蔡自兴,谢斌,唐琎.图像去雾技术研究综述与展望,计算机应用,2010,30(9)：2417-2421
[2]祝培.恶劣天气环境下图像的清晰化：[硕士学位论文].西安：西安理工大学大学,2004
[31詹翔,周焰.一种基于局部方差的雾天图像增强算法,计算机应用,2007,27(2)：510-512
[4]王萍,张春,罗颖听.一种雾天图像对比度增强的快速算法,计算机应用,2006,26(1)：152-154
[5]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.中国图像图形学报,2004,9(1)：124-128
[6]Ming-Jung Seow, Vijayan K. Asari. Ratio rule and homomorphic filter for enhancement of digital colour image. Neurocomputing,2006,69(7):954-958
[7]Fabrizio Russo, An Image Enhancement Technique Combining Sharpening and Noise Reduction, IEEE Transactions on Instrumentation and Measurement,2002, 51(4):824-828
[8]Daniel J.Jobson, Zia-urRahman. Properties and Performance of a Center/Surround Retinex. IEEE Transactions on Image Processing,1997,6(3):451-454
[9]D. Jobson, Z. Rahman, G. Woodell. A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing,1997,6(7):966-972
[10]芮义斌,李鹏,孙锦涛.一种图像去薄雾方法.计算机应用,2006,26(1)：154-156
[11]李林.基于Curvelet变换的SAR图像增强,仪器仪表学报,2006,27(6)：2134-2135
[12]Brian Eriksson. Automatic Image De-Weathering Using Curvelet-Based Vanishing Point Detection. Project report
[13]董涛,董慧颖.基于大气调制传递函数的天气退化图像复原方法研究.沈阳理工大学学报,2006,25(5)：39-42
[14]王挥,刘晓阳.利用大气调制传递函数复原天气退化图像.沈阳航空工业学院学报,2006,23(5)：94-96
[15]T. K. Kim, J.K. Paik, B.S. Kang, Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering, IEEE Transactions on Consumer Electronics,1998,44(1):82-86
[16]J.A. Stark, W.J. Fitzgeralid. An Alternative Algorithm for Adaptive Histogram Equalization. Graphical Models and Image Processing,1996.58(2):180-185
[17]J.A. Stark, Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equlization, IEEE Transactions on Image Processing,2000,9(5): 889-896
[18]Joung-Youn Kim, Lee-Sup Kim, Seung-Ho Hwang, An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization, IEEE Transactions on Circuits and Systems for Video Technology,2001,11(4): 475-484
[19]J.B. Zimmerman, S.M. Pizer, An evaluation of the effectiveness of adapyive histogram equalization for contrast enhancement. IEEE Transactions on Medical Imaging,1988,7(4):304-312
[20]翟艺书,柳晓鸣,涂雅瑗,陈亚宁.一种改进的雾天降质图像的清晰化算法.大连海事大学学报,2007,33(3)：55-58
[21]刘祖军,刘纯亮,梁志虎,张欣.基于动态直方图均匀化的对比度增强方法,光学技术,31(3)：376-379
[22]孙玉宝,肖亮,韦志辉,吴慧中.基于偏微分方程的户外图像去雾方法,系统仿真学报,2007,19(16)：3739-3744
[23]翟艺书,柳晓鸣,涂雅瑗.基于模糊逻辑的雾天降质图像对比度增强算法.计算机应用,2008,28(3)：662-664
[24]J.P. Oakly, B.L. Satherley. Improving image quality in poor visibility conditions using model for degradation. IEEE Transactions on Image Processing,1988,7(2): 167-179
[25]S.G. Narasimhan, S.K. Nayar, Chromatic Framework for Vision in Bad Weather. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA,2000.598-605
[26]S.G.Narasimhan, S.K. Nayar. Vision and the atmosphere. International Journal of Computer Vision,2002,48(3):233-254
[27]Y.Y. Schechner, S.G. Narasimhan, S.K. Nayar, Polarization-based vision through haze, APPLIED OPTICS,2003,42(3):511-525
[28]Y.Y. Schechner, S.G. Narasimhan, S.K. Nayar. Instant dehazing of images using polarization. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2001.325-332
[29]S. Shwartz, E. Namer, Y.Y. Schechner. Blind haze separation. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2006.1984-1991
[30]S G. Narasimhan, S K. Nayer, Contrast Restoration of Weather Degraded Images, IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(6): 713-724
[31]S.G. Narasimhan, S.K. Nayer. Interactive (De)Weathering of an Image using Physical Models. In:Proceedings of IEEE International Conference on Computer Vision Workshop on Color and Photometric Methods in Computer Vision (CPMCV), New York, USA,2003.1-8
[32]J. Kopf, B. Neubert, B. Chen, et al. Deep photo:Model-based photograph enhancement and viewing. ACM Transactions on Graphics,2008,27(5): 116-1-116-10
[33]陈功,王唐,周荷琴.基于物理模型的雾天图像复原新方法.中国图像图形学报,2008,13(5)：888-893
[34]R. Tan. Visibility in bad weather from a sigle image. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA,2008.2347-2354
[35]R. Fattal. Single image dehazing. ACM Transactions on Graphics,2008,27(3): 721-729
[36]Kaiming He, Jian Sun, Xiaoou Tang. Single Image Haze Removal using Dark Channel Prior. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA,2009.1956-1963
[37]Gong Chen, Heqin Zhou, Jiefeng Yan. A Novel Method for Moving Object Detection in Foggy Day. In:Proceeding of Eight ACIS International Conference on Software Engineering. Artificial Intelligence, Networking, and Parallel/Distributed Computing.2007.53-58
[38]Jisha Jone and M.Wilscy, Enhancement of Weather Degraded Video Sequences using Wavelet Fusion. In:Proceedings of 7th IEEE International Conference on Cybernetic Intelligent System.2008.1-6
[39]M. Wilscy, Jisha John. A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images. In:Proceedings of the World Congress on Engineering and Computer Science. San Francisco, USA,2008.2-4
[40]Zhiyuan Xu, Xiaoming Liu, Xiaonan Chen. Fog Removal from Video Sequences Using Contrast Limited Adaptive Histogram Equalization. In:Proceeding of International Conference on Computational Intelligence and Software Engineering. 2009.1-4
[41]张新明,沈兰荪.基于小波和统计特性的自适应图像增强.信号处理,2001,17(3)：227-231
[42]M.Wilscy, Jisha John. A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images. In:Proceedings of the World Congress on Engineering (WCE), London, U.K,2008.98-102
[43]江兴方,王戈,沈为民.多尺度Retinex截断区间对图像质量影响的分析.光学技术,2008,34(1)：69-73
[44]R.G. Hallowell, M atthews M P, Pisano P A. Automated extraction of weather variables from camera imagery. In:Proceedings of Mid-Continent Transportation Research Symposium, Ames,2005.1-13
[45]谢兴尧,万海峰,张速.自校准大气能见度测量方法及系统[P].中国专利,CN200610020115.2，2006.
[46]Taek Mu Kwon, Atmospheric visibility measurement using video cameras: relative visibility[R].Report no. CTS 04-03, University of Minnesota Duluth,2004: 1-44
[47]D. Pomerleau. Visibility estimation from a moving vehicle using the RALPH vision system. In:Proceedings of IEEE Conference on Intelligent Transportation Systems, IEEE press,1997.906-911
[48]吕伟涛,陶善昌,刘亦风等.基于数字摄像技术测量气象能见度——双亮度差方法和试验研究.大气科学,2004,28(4)：559-57
[49]Nicolas Hautiere, Jean-Philippe Tarel, Jean Lavenant, etc. Automatic fog detection and estimation of visibility distance through use of an onboard camera. Machine Vision and Applications,2006,17(1):8-20
[50]N. Hautiere, R. Labayrade, D. Aubert. Real-Time Disparity Contrast Combination for Onboard Estimation of the Visibility Distance. IEEE Transactions on Intelligent Transportation System,2006,7(2):201-212
[51]Clement Boussard, Nicolas Hautiere, Brigitte d'Andrea-Noval. Vision guided by vehicle dynamics for onboard estimation of the visibility range. In:Proceedings of IFAV Symposium on Intelligent Autonomous Vehicles,2007.881-892
[52]Clement Boussard, Nicolas Hautiere, Brigitte d'Andrea-No val. Vehicle dynamics estimation for camera-based visibility distance estimation. In:Proceesings of IEEE/RSJ International conference on intelligent robots and system(IROS), IEEE press,2008.600-605
[53]李勃,董蓉,陈启美.无需人工标记的视频对比度道路能见度检测.计算机辅助设计与图形学学报,2009,21(11)：1576-1582
[54]陈钊正,周庆逵,陈启美.基于小波变换的视频能见度检测算法研究与实现.仪器仪表学报,2010,31(1)：92-98
[55]嵌入式雾天视频清晰化装置系统,上海交通大学路林吉教授课题组,http://www.sble.com.cn/af-wtsp.html
[56]Lee J, Cho J. Effective lane detection and tracking method using statistical modeling of color and lane edge-orientation. Piscataway, NJ, USA.2009.1586-1591
[57]Barnes N, Zelinsky A, Fletcher L S. Real-Time Speed Sign Detection Using the Radial Symmetry Detector. Intelligent Transportation Systems, IEEE Transactions on.2008,9(2):322-332
[58]Garc I A-Garrido M, Sotelo M, Mart I N-Gorostiza E. Fast road sign detection using hough transform for assisted driving of road vehicles. Computer Aided Systems Theory-EUROCAST.2005.543-548
[59]Yuan Tao, Wang Ben. Accelerometer-based Chinese Traffic Police Gesture Recognition System. Chinese Journal of Electronics.2010,19(2):270-274
[60]Jean-Philippe Tarel, Nicolas Hautiere. Fast Visibility Restoration from a Single Color or Gray Level Image. In:Proceedings of IEEE International Conference on Computer Vision, New York, USA,2009.2201-2208
[61]单建华.基于行灰度直方图的直线型车道线识别,视听觉信息的认知计算学术交流会论文集,北京,2010：258-261
[62]唐琎,陈芳艳,谢斌.高速公路禁令标志检测与跟踪,计算机应用研究,2010,27(7)：2760-2762
[63]翟艺书.雾天降质图像的清晰化技术研究：[博士学位论文].大连：大连海事大学,2008
[64]E. J. McCartney. Optics of the Atmosphere:Scattering by molecules and particles. John wiley and sons,1975
[65]Nayar, S.K., Narasimhan, S.G. Vision in bad weather. In:Proceedings of the Seventh IEEE International Conference on Computer Vision.1999.820-827
[66]Narasimhan, S.G. Models and algorithms for vision through the atmosphere[D], Columbia University,2004
[67]刘长盛,刘文宝.大气辐射学.南京：南京大学出版社,1990
[68]N. S. Kopeika, General wavelength dependence of imaging through the atmosphere, Applied optics,1981,20(9):1532-1536
[691吴斌,吴亚东,张红英.基于变分偏微分方程的图像复原技术,北京：北京大学出版社,2008
[70]冈萨雷斯.数字图像处理.北京：电子工业出版社,2007.175-176
[71]Koschmieder, H., Theorie der horizontalen sichewite, Beitr. Phys. Atmos.,1924, 12:33-53.
[72]W. Middelton. Vision through the atmosphere. University of Toronto Press,1952
[73]CIE Publication 17.4 International Lighting Vocabulary[S].
[74]Kohler R. A segmentation system based on thresholding. Graph Model Image Processing,1981,15(2):319-338.
[75]N Hautiere, J-p Tarel, D Aubert, E. Dumont. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology Journal,2008,27(2):87-95.
[76]Eric Dumont, Nicolas Hautiere, Romain Gallen. A Semi-Analytic Model of Fog Effects on Vision. Atmospheric Turbulence, Meteorological Modeling and Aerodynamics.2010,635-670
[77]郭璠,蔡自兴,吴涛.一种新的自动图像去雾方法,国家自然科学基金委”视听觉信息的认知计算”学术交流会论文集,2010,197-202
[78]郭璠,蔡自兴,谢斌,唐琎.单幅图像自动去雾新算法,中国图象图形学报,2011,16(4)：516-521
[79]A. Levin, D.Lischinski, and Y.Weiss. A closed form solution to natural image matting. In:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), USA.2006.61-68
[80]Cardei V, Funt B, Barnard K. White point estimation for uncalibrated images. In: Proceeding of the 7th IS and T/SID Color Imaging Conference:Color Science, Systems and Applications. Scottsdale, USA:Society for Imaging Science and Technology,1997.97-100
[81]禹晶,李大鹏,廖庆敏.基于物理模型的快速单幅图像去雾方法,自动化学报,2011,37(2)：143-149
[82]Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the 6th International Conference on Computer Vision. Bombay, India,1998.839-846
[83]Yang Q X, Tan K H, Ahuja N. Real-time O(1) bilateral filtering. In:proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA,2009.557-564
[84]Paris S, Durand F. A fast approximation of the bilateral filter using a signal processing approach. International Journal of Computer Vision,2009,81(1): 24-52.
[85]郭璠,蔡自兴,谢斌.基于雾气理论的视频去雾算法,电子学报,2011,39(9)：2019-2025
[86]Karlsruhe public database[EB/OL]. http://i21www,ira.uka.de/image_sequences/
[87]蔡超,丁明跃,周成平,张天序.小波域中的双边滤波,电子学报,2004,32(1)：128-131
[88]Dongbin Xu, Chuangbai Xiao. Color-preserving defog method for foggy or haze scenes. In:Proceedings of the 4th International conference on computer vision theory and applications (VISAPP)[C]. Algarve, Portugal,2009.69-73.
[89]Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, et al., Image Quality Assessment:From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing,2004,13(4):600-612
[90]Z. Wang, A. C. Bovik, A universal image quality index, IEEE Signal Processing Letters,2002,9(2):81-84
[91]Z. Wang, A. C. Bovik, L. Lu. Why is image quality assessment so difficult? In Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, vol.4, Orlando, FL,2002.3313-3316
[92]Tao Mei, Xiansheng Hua, Caizhi Zhu, et al., Home Video Visual Quality Assessment with Spatiotemporal Factors. IEEE Transactions on Circuits and Systems for Video Technology,2007,17(6):699-706
[93]Carnec M, Le Callet P, Barba D. Objective quality assessment of color image based on a generic perceptual reference. Image Communication,2008,23(4): 239-256
[94]Sheikh H R, Bovik A C, Cormack L. No-reference quality assessment using natural scene statistics:JPEG 2000. IEEE Transactions on Image Processing,2005, 14(11):1918-1927
[95]禹晶,徐东彬,廖庆敏.图像去雾技术研究进展.中国图像图形学报,2011,16(9)：1561-1576
[96]李大鹏,禹晶,肖创柏.图像去雾的无参考客观质量评测方法.中国图像图形学报,2011,16(9)：1753-1757
[97]姚波,黄磊,刘昌平.去雾增强图像质量客观比较方法研究.中国模式识别会议,中国：IEEE,2009,1-5
[98]Adrian W. Visibility of targets:Model for calculation, Lighting Research & Technology,1989,21(4):181-188
[99]李青,郑南宁,张雪涛,程洪.车载摄像机的一种简易标定方法.机器人,2003,25(7)：626-630
[100]M. Jourlin, J.C. Pinoli. Logarithmic image processing. Adv Imag Elect Phys. 2001,115:129-94
[101]周燕艳,张红莉.3ds max从入门到精通.北京：中国电力出版社,2007.258-266
[102]马鑫,中文版3ds Max 2010超级手册,北京：清华大学出版社,2010,89-115
[103]Rossum Z, Nieuwenhuizn T. Multiple scattering of classical waves:microscopy, mesoscopy and diffusion. Reviews of Modern Physics,1999,71(1):313-371
[104]Yendrikhovskij S, Blommaert F, Ridder H. de. Perceptual optimal color reproduction. In:Proceedings of SPIE:Human vision and electronic imaging III, Volume 3299, San Jose, CA, USA,1998.26-29
[105]Kaiqi Huang, Qiao Wang, Zhenyang Wu. Natural color image enhancement and evaluation algorithm based on human visual system. Computer Vision and Image Understanding.2006,52-63
[106]S. Hasler, S. Susstrunk, Measuring colorfulness in real images, Proc. SPIE Electron. Imag.:Hum. Vision Electron. Imag. VIII, SPIE5007,2003:87-95
[107]史忠科,曹力.交通图像检测与分析,北京：科学出版社,2007,243-244
[108]Meghna Singh, Mrinal Mandal and Anup Basu. Visual gesture recognition for ground air traffic control using the Radon transform. In:Proceedings of IEEE/RSJ IROS,2005
[109]Beiji Zou, Shu Chen, Cao Shi, Umugwaneza Marie Providence. Automatic reconstruction of 3D human motion poses from uncalibrated monocular video sequences based on markerless human motion tracking. Pattern Recognition,2009, 42:1559-1571
[110]M. Andriluka, S. Roth. Pictorial structures revisited:People detection and articulated pose estimation. In:Proceedings of CVPR,2009.1014-1021
[111]Sam Johnson, Mark Everigham. Learning effective human pose estimation from inacurate annotation. In:Proceedings of CVPR,2011.1465-1472
[112]Mun Wai Lee, Ram Nevatia. Body Part Detection for Human Pose Estimation and Tracking. In:Proceedings of IEEE workshop on motion and video computing (WMVC),2007.23-28
[113]F. Guo, Z.X. Cai, B. Xie, J. Tang. Automatic Image Haze Removal Based on Luminance Component, In:Proceedings of SIP,2010.1-4
[114]M. D. Levin, Vision in man and machine. New York:McGraw-Hill,1985
[115]Ahmed Elgammal, Ramani Durauswami, David Harwood, Larry S. Davis. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. In:Proceedings of the IEEE,2002,90(7): 1151-1163
[116]中国公安部网站[EB/OL], http://www.mps.gov.cn/n16/index.html
[2]祝培.恶劣天气环境下图像的清晰化：[硕士学位论文].西安：西安理工大学大学,2004
[31詹翔,周焰.一种基于局部方差的雾天图像增强算法,计算机应用,2007,27(2)：510-512
[4]王萍,张春,罗颖听.一种雾天图像对比度增强的快速算法,计算机应用,2006,26(1)：152-154
[5]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.中国图像图形学报,2004,9(1)：124-128
[6]Ming-Jung Seow, Vijayan K. Asari. Ratio rule and homomorphic filter for enhancement of digital colour image. Neurocomputing,2006,69(7):954-958
[7]Fabrizio Russo, An Image Enhancement Technique Combining Sharpening and Noise Reduction, IEEE Transactions on Instrumentation and Measurement,2002, 51(4):824-828
[8]Daniel J.Jobson, Zia-urRahman. Properties and Performance of a Center/Surround Retinex. IEEE Transactions on Image Processing,1997,6(3):451-454
[9]D. Jobson, Z. Rahman, G. Woodell. A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing,1997,6(7):966-972
[10]芮义斌,李鹏,孙锦涛.一种图像去薄雾方法.计算机应用,2006,26(1)：154-156
[11]李林.基于Curvelet变换的SAR图像增强,仪器仪表学报,2006,27(6)：2134-2135
[12]Brian Eriksson. Automatic Image De-Weathering Using Curvelet-Based Vanishing Point Detection. Project report
[13]董涛,董慧颖.基于大气调制传递函数的天气退化图像复原方法研究.沈阳理工大学学报,2006,25(5)：39-42
[14]王挥,刘晓阳.利用大气调制传递函数复原天气退化图像.沈阳航空工业学院学报,2006,23(5)：94-96
[15]T. K. Kim, J.K. Paik, B.S. Kang, Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering, IEEE Transactions on Consumer Electronics,1998,44(1):82-86
[16]J.A. Stark, W.J. Fitzgeralid. An Alternative Algorithm for Adaptive Histogram Equalization. Graphical Models and Image Processing,1996.58(2):180-185
[17]J.A. Stark, Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equlization, IEEE Transactions on Image Processing,2000,9(5): 889-896
[18]Joung-Youn Kim, Lee-Sup Kim, Seung-Ho Hwang, An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization, IEEE Transactions on Circuits and Systems for Video Technology,2001,11(4): 475-484
[19]J.B. Zimmerman, S.M. Pizer, An evaluation of the effectiveness of adapyive histogram equalization for contrast enhancement. IEEE Transactions on Medical Imaging,1988,7(4):304-312
[20]翟艺书,柳晓鸣,涂雅瑗,陈亚宁.一种改进的雾天降质图像的清晰化算法.大连海事大学学报,2007,33(3)：55-58
[21]刘祖军,刘纯亮,梁志虎,张欣.基于动态直方图均匀化的对比度增强方法,光学技术,31(3)：376-379
[22]孙玉宝,肖亮,韦志辉,吴慧中.基于偏微分方程的户外图像去雾方法,系统仿真学报,2007,19(16)：3739-3744
[23]翟艺书,柳晓鸣,涂雅瑗.基于模糊逻辑的雾天降质图像对比度增强算法.计算机应用,2008,28(3)：662-664
[24]J.P. Oakly, B.L. Satherley. Improving image quality in poor visibility conditions using model for degradation. IEEE Transactions on Image Processing,1988,7(2): 167-179
[25]S.G. Narasimhan, S.K. Nayar, Chromatic Framework for Vision in Bad Weather. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA,2000.598-605
[26]S.G.Narasimhan, S.K. Nayar. Vision and the atmosphere. International Journal of Computer Vision,2002,48(3):233-254
[27]Y.Y. Schechner, S.G. Narasimhan, S.K. Nayar, Polarization-based vision through haze, APPLIED OPTICS,2003,42(3):511-525
[28]Y.Y. Schechner, S.G. Narasimhan, S.K. Nayar. Instant dehazing of images using polarization. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2001.325-332
[29]S. Shwartz, E. Namer, Y.Y. Schechner. Blind haze separation. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2006.1984-1991
[30]S G. Narasimhan, S K. Nayer, Contrast Restoration of Weather Degraded Images, IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(6): 713-724
[31]S.G. Narasimhan, S.K. Nayer. Interactive (De)Weathering of an Image using Physical Models. In:Proceedings of IEEE International Conference on Computer Vision Workshop on Color and Photometric Methods in Computer Vision (CPMCV), New York, USA,2003.1-8
[32]J. Kopf, B. Neubert, B. Chen, et al. Deep photo:Model-based photograph enhancement and viewing. ACM Transactions on Graphics,2008,27(5): 116-1-116-10
[33]陈功,王唐,周荷琴.基于物理模型的雾天图像复原新方法.中国图像图形学报,2008,13(5)：888-893
[34]R. Tan. Visibility in bad weather from a sigle image. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA,2008.2347-2354
[35]R. Fattal. Single image dehazing. ACM Transactions on Graphics,2008,27(3): 721-729
[36]Kaiming He, Jian Sun, Xiaoou Tang. Single Image Haze Removal using Dark Channel Prior. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA,2009.1956-1963
[37]Gong Chen, Heqin Zhou, Jiefeng Yan. A Novel Method for Moving Object Detection in Foggy Day. In:Proceeding of Eight ACIS International Conference on Software Engineering. Artificial Intelligence, Networking, and Parallel/Distributed Computing.2007.53-58
[38]Jisha Jone and M.Wilscy, Enhancement of Weather Degraded Video Sequences using Wavelet Fusion. In:Proceedings of 7th IEEE International Conference on Cybernetic Intelligent System.2008.1-6
[39]M. Wilscy, Jisha John. A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images. In:Proceedings of the World Congress on Engineering and Computer Science. San Francisco, USA,2008.2-4
[40]Zhiyuan Xu, Xiaoming Liu, Xiaonan Chen. Fog Removal from Video Sequences Using Contrast Limited Adaptive Histogram Equalization. In:Proceeding of International Conference on Computational Intelligence and Software Engineering. 2009.1-4
[41]张新明,沈兰荪.基于小波和统计特性的自适应图像增强.信号处理,2001,17(3)：227-231
[42]M.Wilscy, Jisha John. A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images. In:Proceedings of the World Congress on Engineering (WCE), London, U.K,2008.98-102
[43]江兴方,王戈,沈为民.多尺度Retinex截断区间对图像质量影响的分析.光学技术,2008,34(1)：69-73
[44]R.G. Hallowell, M atthews M P, Pisano P A. Automated extraction of weather variables from camera imagery. In:Proceedings of Mid-Continent Transportation Research Symposium, Ames,2005.1-13
[45]谢兴尧,万海峰,张速.自校准大气能见度测量方法及系统[P].中国专利,CN200610020115.2，2006.
[46]Taek Mu Kwon, Atmospheric visibility measurement using video cameras: relative visibility[R].Report no. CTS 04-03, University of Minnesota Duluth,2004: 1-44
[47]D. Pomerleau. Visibility estimation from a moving vehicle using the RALPH vision system. In:Proceedings of IEEE Conference on Intelligent Transportation Systems, IEEE press,1997.906-911
[48]吕伟涛,陶善昌,刘亦风等.基于数字摄像技术测量气象能见度——双亮度差方法和试验研究.大气科学,2004,28(4)：559-57
[49]Nicolas Hautiere, Jean-Philippe Tarel, Jean Lavenant, etc. Automatic fog detection and estimation of visibility distance through use of an onboard camera. Machine Vision and Applications,2006,17(1):8-20
[50]N. Hautiere, R. Labayrade, D. Aubert. Real-Time Disparity Contrast Combination for Onboard Estimation of the Visibility Distance. IEEE Transactions on Intelligent Transportation System,2006,7(2):201-212
[51]Clement Boussard, Nicolas Hautiere, Brigitte d'Andrea-Noval. Vision guided by vehicle dynamics for onboard estimation of the visibility range. In:Proceedings of IFAV Symposium on Intelligent Autonomous Vehicles,2007.881-892
[52]Clement Boussard, Nicolas Hautiere, Brigitte d'Andrea-No val. Vehicle dynamics estimation for camera-based visibility distance estimation. In:Proceesings of IEEE/RSJ International conference on intelligent robots and system(IROS), IEEE press,2008.600-605
[53]李勃,董蓉,陈启美.无需人工标记的视频对比度道路能见度检测.计算机辅助设计与图形学学报,2009,21(11)：1576-1582
[54]陈钊正,周庆逵,陈启美.基于小波变换的视频能见度检测算法研究与实现.仪器仪表学报,2010,31(1)：92-98
[55]嵌入式雾天视频清晰化装置系统,上海交通大学路林吉教授课题组,http://www.sble.com.cn/af-wtsp.html
[56]Lee J, Cho J. Effective lane detection and tracking method using statistical modeling of color and lane edge-orientation. Piscataway, NJ, USA.2009.1586-1591
[57]Barnes N, Zelinsky A, Fletcher L S. Real-Time Speed Sign Detection Using the Radial Symmetry Detector. Intelligent Transportation Systems, IEEE Transactions on.2008,9(2):322-332
[58]Garc I A-Garrido M, Sotelo M, Mart I N-Gorostiza E. Fast road sign detection using hough transform for assisted driving of road vehicles. Computer Aided Systems Theory-EUROCAST.2005.543-548
[59]Yuan Tao, Wang Ben. Accelerometer-based Chinese Traffic Police Gesture Recognition System. Chinese Journal of Electronics.2010,19(2):270-274
[60]Jean-Philippe Tarel, Nicolas Hautiere. Fast Visibility Restoration from a Single Color or Gray Level Image. In:Proceedings of IEEE International Conference on Computer Vision, New York, USA,2009.2201-2208
[61]单建华.基于行灰度直方图的直线型车道线识别,视听觉信息的认知计算学术交流会论文集,北京,2010：258-261
[62]唐琎,陈芳艳,谢斌.高速公路禁令标志检测与跟踪,计算机应用研究,2010,27(7)：2760-2762
[63]翟艺书.雾天降质图像的清晰化技术研究：[博士学位论文].大连：大连海事大学,2008
[64]E. J. McCartney. Optics of the Atmosphere:Scattering by molecules and particles. John wiley and sons,1975
[65]Nayar, S.K., Narasimhan, S.G. Vision in bad weather. In:Proceedings of the Seventh IEEE International Conference on Computer Vision.1999.820-827
[66]Narasimhan, S.G. Models and algorithms for vision through the atmosphere[D], Columbia University,2004
[67]刘长盛,刘文宝.大气辐射学.南京：南京大学出版社,1990
[68]N. S. Kopeika, General wavelength dependence of imaging through the atmosphere, Applied optics,1981,20(9):1532-1536
[691吴斌,吴亚东,张红英.基于变分偏微分方程的图像复原技术,北京：北京大学出版社,2008
[70]冈萨雷斯.数字图像处理.北京：电子工业出版社,2007.175-176
[71]Koschmieder, H., Theorie der horizontalen sichewite, Beitr. Phys. Atmos.,1924, 12:33-53.
[72]W. Middelton. Vision through the atmosphere. University of Toronto Press,1952
[73]CIE Publication 17.4 International Lighting Vocabulary[S].
[74]Kohler R. A segmentation system based on thresholding. Graph Model Image Processing,1981,15(2):319-338.
[75]N Hautiere, J-p Tarel, D Aubert, E. Dumont. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology Journal,2008,27(2):87-95.
[76]Eric Dumont, Nicolas Hautiere, Romain Gallen. A Semi-Analytic Model of Fog Effects on Vision. Atmospheric Turbulence, Meteorological Modeling and Aerodynamics.2010,635-670
[77]郭璠,蔡自兴,吴涛.一种新的自动图像去雾方法,国家自然科学基金委”视听觉信息的认知计算”学术交流会论文集,2010,197-202
[78]郭璠,蔡自兴,谢斌,唐琎.单幅图像自动去雾新算法,中国图象图形学报,2011,16(4)：516-521
[79]A. Levin, D.Lischinski, and Y.Weiss. A closed form solution to natural image matting. In:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), USA.2006.61-68
[80]Cardei V, Funt B, Barnard K. White point estimation for uncalibrated images. In: Proceeding of the 7th IS and T/SID Color Imaging Conference:Color Science, Systems and Applications. Scottsdale, USA:Society for Imaging Science and Technology,1997.97-100
[81]禹晶,李大鹏,廖庆敏.基于物理模型的快速单幅图像去雾方法,自动化学报,2011,37(2)：143-149
[82]Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the 6th International Conference on Computer Vision. Bombay, India,1998.839-846
[83]Yang Q X, Tan K H, Ahuja N. Real-time O(1) bilateral filtering. In:proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA,2009.557-564
[84]Paris S, Durand F. A fast approximation of the bilateral filter using a signal processing approach. International Journal of Computer Vision,2009,81(1): 24-52.
[85]郭璠,蔡自兴,谢斌.基于雾气理论的视频去雾算法,电子学报,2011,39(9)：2019-2025
[86]Karlsruhe public database[EB/OL]. http://i21www,ira.uka.de/image_sequences/
[87]蔡超,丁明跃,周成平,张天序.小波域中的双边滤波,电子学报,2004,32(1)：128-131
[88]Dongbin Xu, Chuangbai Xiao. Color-preserving defog method for foggy or haze scenes. In:Proceedings of the 4th International conference on computer vision theory and applications (VISAPP)[C]. Algarve, Portugal,2009.69-73.
[89]Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, et al., Image Quality Assessment:From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing,2004,13(4):600-612
[90]Z. Wang, A. C. Bovik, A universal image quality index, IEEE Signal Processing Letters,2002,9(2):81-84
[91]Z. Wang, A. C. Bovik, L. Lu. Why is image quality assessment so difficult? In Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, vol.4, Orlando, FL,2002.3313-3316
[92]Tao Mei, Xiansheng Hua, Caizhi Zhu, et al., Home Video Visual Quality Assessment with Spatiotemporal Factors. IEEE Transactions on Circuits and Systems for Video Technology,2007,17(6):699-706
[93]Carnec M, Le Callet P, Barba D. Objective quality assessment of color image based on a generic perceptual reference. Image Communication,2008,23(4): 239-256
[94]Sheikh H R, Bovik A C, Cormack L. No-reference quality assessment using natural scene statistics:JPEG 2000. IEEE Transactions on Image Processing,2005, 14(11):1918-1927
[95]禹晶,徐东彬,廖庆敏.图像去雾技术研究进展.中国图像图形学报,2011,16(9)：1561-1576
[96]李大鹏,禹晶,肖创柏.图像去雾的无参考客观质量评测方法.中国图像图形学报,2011,16(9)：1753-1757
[97]姚波,黄磊,刘昌平.去雾增强图像质量客观比较方法研究.中国模式识别会议,中国：IEEE,2009,1-5
[98]Adrian W. Visibility of targets:Model for calculation, Lighting Research & Technology,1989,21(4):181-188
[99]李青,郑南宁,张雪涛,程洪.车载摄像机的一种简易标定方法.机器人,2003,25(7)：626-630
[100]M. Jourlin, J.C. Pinoli. Logarithmic image processing. Adv Imag Elect Phys. 2001,115:129-94
[101]周燕艳,张红莉.3ds max从入门到精通.北京：中国电力出版社,2007.258-266
[102]马鑫,中文版3ds Max 2010超级手册,北京：清华大学出版社,2010,89-115
[103]Rossum Z, Nieuwenhuizn T. Multiple scattering of classical waves:microscopy, mesoscopy and diffusion. Reviews of Modern Physics,1999,71(1):313-371
[104]Yendrikhovskij S, Blommaert F, Ridder H. de. Perceptual optimal color reproduction. In:Proceedings of SPIE:Human vision and electronic imaging III, Volume 3299, San Jose, CA, USA,1998.26-29
[105]Kaiqi Huang, Qiao Wang, Zhenyang Wu. Natural color image enhancement and evaluation algorithm based on human visual system. Computer Vision and Image Understanding.2006,52-63
[106]S. Hasler, S. Susstrunk, Measuring colorfulness in real images, Proc. SPIE Electron. Imag.:Hum. Vision Electron. Imag. VIII, SPIE5007,2003:87-95
[107]史忠科,曹力.交通图像检测与分析,北京：科学出版社,2007,243-244
[108]Meghna Singh, Mrinal Mandal and Anup Basu. Visual gesture recognition for ground air traffic control using the Radon transform. In:Proceedings of IEEE/RSJ IROS,2005
[109]Beiji Zou, Shu Chen, Cao Shi, Umugwaneza Marie Providence. Automatic reconstruction of 3D human motion poses from uncalibrated monocular video sequences based on markerless human motion tracking. Pattern Recognition,2009, 42:1559-1571
[110]M. Andriluka, S. Roth. Pictorial structures revisited:People detection and articulated pose estimation. In:Proceedings of CVPR,2009.1014-1021
[111]Sam Johnson, Mark Everigham. Learning effective human pose estimation from inacurate annotation. In:Proceedings of CVPR,2011.1465-1472
[112]Mun Wai Lee, Ram Nevatia. Body Part Detection for Human Pose Estimation and Tracking. In:Proceedings of IEEE workshop on motion and video computing (WMVC),2007.23-28
[113]F. Guo, Z.X. Cai, B. Xie, J. Tang. Automatic Image Haze Removal Based on Luminance Component, In:Proceedings of SIP,2010.1-4
[114]M. D. Levin, Vision in man and machine. New York:McGraw-Hill,1985
[115]Ahmed Elgammal, Ramani Durauswami, David Harwood, Larry S. Davis. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. In:Proceedings of the IEEE,2002,90(7): 1151-1163
[116]中国公安部网站[EB/OL], http://www.mps.gov.cn/n16/index.html
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