粉尘环境中的图像恢复研究
|
摘要
粉尘环境中的图像恢复是一项开创性的工作,目前这方面的研究成果甚少,然而这一问题却有着重要的研究意义,其研究成果可以广泛地应用于救灾、交通、安全生产、农业乃至军事等领域。本文针对粉尘环境中的图像恢复开展了如下研究：
(1)对粉尘这种常见的物质进行了概述,对粉尘进行了定义与分类,指出了粉尘所具有的物理特性。用实例说明粉尘对图像亮度、对比度与清晰度的影响。目前,由于对雾、霾环境中图像清晰化研究成果较多,因此,作者对雾、霾环境中图像清晰化现状进行了较为详细的综述与分析,目的是考察此类图像清晰化的研究思路是否对粉尘环境中图像恢复的研究能够有所借鉴。最后形成粉尘环境中图像恢复的研究方法。
(2)粉尘对图像的影响源于粉尘对光线的散射与吸收,因此本文首先对单个粉尘颗粒相关光散射特性进行了研究,从理论上对单个粉尘颗粒产生的散射光强、散射光传播方向以及颗粒对入射光能量的吸收情况进行了分析,并通过自编的Mie散射程序加以模拟验证,进而得出单个粉尘颗粒的相关光散射特性,为建立粉尘环境中图像的退化模型提供了光学理论依据。
(3)在单颗粉尘光散射特性的研究基础上,进一步开展了光线在粉尘介质中的传输研究,首先,对粉尘介质进行了独立性与同质均匀性假设,在此基础上,用多散射理论对粉尘的光传输过程进行了理论分析,找出了光强在传输过程中发生变化的规律,以及对成像的影响；然后,本文提出了基于一级多散射模型的粉尘环境中的图像退化模型；最后,用此图像退化模型进行了图像的合成,为验证粉尘环境中图像恢复算法提供了实验素材。
(4)根据粉尘环境中图像退化模型,研究了粉尘图像恢复算法。首先介绍了暗元色原理,用实验证明了暗元色在自然界中的广泛存在；其次,在粉尘环境中图像退化模型的基础上应用暗元色原理推出了基于单幅图像的图像恢复公式,同时也得出了图像的深度图；接着,通过实验说明了图像恢复公式中各个参数对图像恢复效果的影响；然后,根据客观图像质量评价理论,提出了一种新的基于阈值-Kirsch算子的图像质量评价函数,并以此为标准,应用GA算法对各个参数进行优化,从而得出了最优的图像恢复结果；最后,用实验证明了本算法有效地去除了图像中粉尘的影响,揭示了更多的边缘信息,为目标自动识别提供了依据。
(5)将粉尘环境中的图像恢复算法进行了应用研究。使用可见光视觉传感器的救援机器人,往往会遇到粉尘环境的干扰,这给救援机器人的障碍物检测与识别造成了困难。本文通过像机的几何模型与粉尘环境中图像的深度图,实现了用单像机、单幅图像,对任意形状,任意时刻障碍物的距离检测,并由此得到图像中每一点相对机器人的三维坐标。此外,通过粉尘环境中的深度图,还可以实现对图像中深度边缘的识别,从而实现对障碍物检测的目的。最后,本文通过实验,验证了这种方法的可行性与有效性,为救援机器人在粉尘环境中识别障碍物,提供了一种简单经济的方法。
Image restoration in dust environment is a creative work, and so far little research in this area has been discovered, but the problem is of its research significance. The research fruit can be widely adopted in the fields of rescue, traffic, production safety, agriculture and military. And the research on image restoration in dust environment in the dissertation has been carried out as follows:
The dust as a kind of common material was summarized. Its definition and classification have been stated, as well as the physical characters. With examples, the dust on image brightness, contrast and sharpness are also stated here. Currently, due to fog, haze, clear image of the environment results in more, the author gives the overview and analysis on current status of the research, in order to learn from it and carry on the research on image restoration in dust environment. Finally, a method of research on image restoration in dust environment formed.
Light scattering and absorption by dust particles is main reason for image degradation. So a research on the optical characters of a single dust particle was earried on in the dissertation. First of all, a theoretical analysis on light scattering intensity scattered by single dust particle, its propagation direction and energy absorption by dust particle was given. Then, the progress was simulated by Mie scattering programe. Finally, the optical characters of single dust particle were obtained from simulated results. The research provides the optical basis for establishing the image degradation model in the dust environment.
On the basis of optical characters of single dust particle, the research on light transfer in the dust medium was discussed. First, two hypothesis were made for the dust medium, one was the hypothesis of independent and the other was the hypothesis of homogeneity. Second, the multi-scattering theory was used to give a theoretical analysis on the process of light transfer through the dust, and the way of luminous intensity variety in the light transfer process was discovered, as well as to image formation. Then, the image degradation model in dust environment based on the first order multi-scattering model was proposed. Finally, the image degradation model was used to synthesis the image, which provides experimental source material for image restoration in dust environment.
According to the image degradation model, an algorithm of image restoration in dust environment was studied. First, the principle of the dark channel prior was introduced, meanwhile, its existence extensively in the nature was also proved with the experiment. Secondly, on the basis of image degradation model in dust environment, the dark channel prior was applied and single image restoration equation was deduced as well as depth map. And then, we explained the influence of each parameter in the image resumes formulae upon the result of image resumes influence through the experiment. Then, according to image objective quality assessment theory, a new image quality assessment function based on Threshold-Kirsch operator was proposed; it was used as a criterian for genetic algorithm to evaluate the parameters.then an optimal image restoration result was achieved. Finally, the algorithm was proved to be effective on dedust, and was able to recover detailed edge of the image. The restoration result provides basis for automatic objects recognition.
Besides, we also made a research of the application of image restoration in dust environment. As we know, optical sensors on the rescue robot are often disturbed by dust, and this causes a few troubles for the obstacles detection and recognition. In order to solve this problem, a method of obstacle detection and recognition which is based on camera geometry model and the depth chart in dust environment was adapted and it could detect obstacles of arbitrary shape at any time with only a single camera and a single image. The robot's three dimensional coordinates can be obtained via the method, too. Therefore, based on it, in the way of the depth edge detected we were able to segregate the obstacles from the background. Finally, the validity and feasibility of the method was fully demonstrated by the experiments. The method provides the rescue robot a simple and economical way to detect obstacles in dust environment.
(1)对粉尘这种常见的物质进行了概述,对粉尘进行了定义与分类,指出了粉尘所具有的物理特性。用实例说明粉尘对图像亮度、对比度与清晰度的影响。目前,由于对雾、霾环境中图像清晰化研究成果较多,因此,作者对雾、霾环境中图像清晰化现状进行了较为详细的综述与分析,目的是考察此类图像清晰化的研究思路是否对粉尘环境中图像恢复的研究能够有所借鉴。最后形成粉尘环境中图像恢复的研究方法。
(2)粉尘对图像的影响源于粉尘对光线的散射与吸收,因此本文首先对单个粉尘颗粒相关光散射特性进行了研究,从理论上对单个粉尘颗粒产生的散射光强、散射光传播方向以及颗粒对入射光能量的吸收情况进行了分析,并通过自编的Mie散射程序加以模拟验证,进而得出单个粉尘颗粒的相关光散射特性,为建立粉尘环境中图像的退化模型提供了光学理论依据。
(3)在单颗粉尘光散射特性的研究基础上,进一步开展了光线在粉尘介质中的传输研究,首先,对粉尘介质进行了独立性与同质均匀性假设,在此基础上,用多散射理论对粉尘的光传输过程进行了理论分析,找出了光强在传输过程中发生变化的规律,以及对成像的影响；然后,本文提出了基于一级多散射模型的粉尘环境中的图像退化模型；最后,用此图像退化模型进行了图像的合成,为验证粉尘环境中图像恢复算法提供了实验素材。
(4)根据粉尘环境中图像退化模型,研究了粉尘图像恢复算法。首先介绍了暗元色原理,用实验证明了暗元色在自然界中的广泛存在；其次,在粉尘环境中图像退化模型的基础上应用暗元色原理推出了基于单幅图像的图像恢复公式,同时也得出了图像的深度图；接着,通过实验说明了图像恢复公式中各个参数对图像恢复效果的影响；然后,根据客观图像质量评价理论,提出了一种新的基于阈值-Kirsch算子的图像质量评价函数,并以此为标准,应用GA算法对各个参数进行优化,从而得出了最优的图像恢复结果；最后,用实验证明了本算法有效地去除了图像中粉尘的影响,揭示了更多的边缘信息,为目标自动识别提供了依据。
(5)将粉尘环境中的图像恢复算法进行了应用研究。使用可见光视觉传感器的救援机器人,往往会遇到粉尘环境的干扰,这给救援机器人的障碍物检测与识别造成了困难。本文通过像机的几何模型与粉尘环境中图像的深度图,实现了用单像机、单幅图像,对任意形状,任意时刻障碍物的距离检测,并由此得到图像中每一点相对机器人的三维坐标。此外,通过粉尘环境中的深度图,还可以实现对图像中深度边缘的识别,从而实现对障碍物检测的目的。最后,本文通过实验,验证了这种方法的可行性与有效性,为救援机器人在粉尘环境中识别障碍物,提供了一种简单经济的方法。
Image restoration in dust environment is a creative work, and so far little research in this area has been discovered, but the problem is of its research significance. The research fruit can be widely adopted in the fields of rescue, traffic, production safety, agriculture and military. And the research on image restoration in dust environment in the dissertation has been carried out as follows:
The dust as a kind of common material was summarized. Its definition and classification have been stated, as well as the physical characters. With examples, the dust on image brightness, contrast and sharpness are also stated here. Currently, due to fog, haze, clear image of the environment results in more, the author gives the overview and analysis on current status of the research, in order to learn from it and carry on the research on image restoration in dust environment. Finally, a method of research on image restoration in dust environment formed.
Light scattering and absorption by dust particles is main reason for image degradation. So a research on the optical characters of a single dust particle was earried on in the dissertation. First of all, a theoretical analysis on light scattering intensity scattered by single dust particle, its propagation direction and energy absorption by dust particle was given. Then, the progress was simulated by Mie scattering programe. Finally, the optical characters of single dust particle were obtained from simulated results. The research provides the optical basis for establishing the image degradation model in the dust environment.
On the basis of optical characters of single dust particle, the research on light transfer in the dust medium was discussed. First, two hypothesis were made for the dust medium, one was the hypothesis of independent and the other was the hypothesis of homogeneity. Second, the multi-scattering theory was used to give a theoretical analysis on the process of light transfer through the dust, and the way of luminous intensity variety in the light transfer process was discovered, as well as to image formation. Then, the image degradation model in dust environment based on the first order multi-scattering model was proposed. Finally, the image degradation model was used to synthesis the image, which provides experimental source material for image restoration in dust environment.
According to the image degradation model, an algorithm of image restoration in dust environment was studied. First, the principle of the dark channel prior was introduced, meanwhile, its existence extensively in the nature was also proved with the experiment. Secondly, on the basis of image degradation model in dust environment, the dark channel prior was applied and single image restoration equation was deduced as well as depth map. And then, we explained the influence of each parameter in the image resumes formulae upon the result of image resumes influence through the experiment. Then, according to image objective quality assessment theory, a new image quality assessment function based on Threshold-Kirsch operator was proposed; it was used as a criterian for genetic algorithm to evaluate the parameters.then an optimal image restoration result was achieved. Finally, the algorithm was proved to be effective on dedust, and was able to recover detailed edge of the image. The restoration result provides basis for automatic objects recognition.
Besides, we also made a research of the application of image restoration in dust environment. As we know, optical sensors on the rescue robot are often disturbed by dust, and this causes a few troubles for the obstacles detection and recognition. In order to solve this problem, a method of obstacle detection and recognition which is based on camera geometry model and the depth chart in dust environment was adapted and it could detect obstacles of arbitrary shape at any time with only a single camera and a single image. The robot's three dimensional coordinates can be obtained via the method, too. Therefore, based on it, in the way of the depth edge detected we were able to segregate the obstacles from the background. Finally, the validity and feasibility of the method was fully demonstrated by the experiments. The method provides the rescue robot a simple and economical way to detect obstacles in dust environment.
引文
[1]杨胜强.粉尘防治理论及技术[M].徐州：中国矿业大学出版社,2007：1-8.
[2]郭艳荣,鲜继清,吕霞付,谢正祥.基于视觉感知的最好质量图像评价方法[J].重庆科技学院学报(自然科学版),2009,11(6)：151-153.
[3]陈文兵,杨建伟,张小磊.能见度自动化计算方法[J].计算机工程与应用,2010,46(30)：181-183.
[4]Robby T. Tan. Visibility in BadWeather from a Single Image[C]. CVPR, USA: Anchorage,2008:1-8.
[5]郭璠,蔡自兴,谢斌,唐琎.图像去雾技术研究综述与展望[J].计算机应用,2010,30(9)：2417-2421.
[6]S. M. Pizer et al. Adaptive histogram equalization and itsvariations[J] Comput. Vis., Graph., Image Process.,1987,39:355-368.
[7]K. Zuiderveld. Contrast limited adaptive histogram equalization[C]. in Graphics Gems IV, P. Heckbert, Ed. USA:New York,1994:474-485.
[8]J. A. Stark. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEETrans. Image Process,2000,9(5):889-896.
[9]A. Polesel, G. Ramponi, V. J. Mathews. Image enhancement via adaptive unsharp masking[J]. IEEE Trans. Image Process.,2000,9 (3):505-510.
[10]王萍,张春,罗颖听.一种雾天图像低对比度增强的快速算法[J].计算机应用,2006,26(1)：152156.
[11]艾明晶,戴隆忠,曹庆华.雾天环境下自适应图像增强去雾方法研究[J].计算机仿真,2009,26(7)：244247.
[12]Y. S. Zhai, X. M. Liu. An improved fog-degraded image enhancement algorithm [C]. Wavelet Analysis and Pattern Recognition, China:Bejing,2007:522-526.
[13]詹翔,周焰.一种基于局部方差的雾天图像增强方法[J].计算机应用.2007,27(2)：510-512.
[14]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,2004,9(1)：124128.
[15]刘惠燕,何文章,马云飞.基于数学形态学的雾天图像增强算法[J].天津工程师范学院学报,2009,19(3)：34-35.
[16]Madasu Hanmandlu, Devendra Jha. An Optimal Fuzzy System for Color Image Enhancement[J]. IEEE Transactions on Image Processing,2006,15 (10): 2956-2966.
[17]翟艺书,柳晓鸣,涂雅瑗.基于模糊逻辑的雾天退化图像对比度增强算法[J].计算机应用,2008,28(3)：662-664.
[18]翟艺书,梁媛.基于混合对比度增强的户外图像去雾方法[J].计算机仿真,2010,27(5)：227-230.
[19]D. J. Jobson, Z. Rahman, G. A. Woodell. Properties and performance of a center/surround ret inex [J]. IEEE Trans. Image Process,1997,6(3):451-462.
[20]Daniel J. Jobson, Zia-ur Rahman, Glenn A. Woodell. A multi-scale retinex for bridging the gap between color images and the human observation of scenes [J]. IEEE Transaction on Image Process,1997,6 (7):965-976.
[21]Zia-ur. Rahman, Daniel. Jobson, Glenn A. Woodell. Retinex processing for automatic image enhancement [J]. Journal of Electronic Imaging,2004,13 (1): 100-110.
[22]李鹏.薄尘雾退化图像的处理研究[D]南京：南京理工大学,2005.
[23]刘瑞剑.低能见度条件下图像清晰化处理研究[D].太原：中北大学,2008.
[24]芮义斌,李鹏,孙锦涛.基于色彩恒常理论的图像去雾技术[J].南京理工大学学报,2006,30(5)：622-625.
[25]周树道,邵啸,朱涛,黄峰.薄雾影响下的退化彩色图像处理方法[J].解放军理工大学学报(自然科学版),2008,19(2)：142-145.
[26]P. Scheunders. A multivalued image wavelet representation based on multiscale fundamental forms [J]. IEEE Trans. Image Process,2002,10 (5):568-575.
[27]朱锡芳,徐芳芳. Retinex和小波变换去除遥感图像云雾方法分析[J].常州工学院学报,2007,20 (5)：8-13.
[28]Jean-Luc Starck, Fionn Murtagh, Emmanuel J. Candes, David L. Donoho. Gray and Color Image Contrast Enhancement by the Curvelet Transform [J]. IEEE Transactions on Image Processing.,2003,12 (6):706-717.
[29]L. Grewe, R. R. Brooks. Atmospheric attenuation reduction through multi-sensor fusion [C]. Proc. SPIE,1998,3376:102-109.
[30]Lex Schaul, Clement Fredembach, Sabinc Susstrunk. Color Image Dehazing Using The Near-Infrared [C].16th IEEE International Conference on Image Processing. Cairo:Egyp,2009:1629-1632.
[31]M. Wilscy, Jisha John. A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images [C]. Proceedings of the World Congress on Engineering. U.K:London,2008:705-709.
[32]Grewe.Lynne L.; Brooks. Richard R. Atmospheric attenuation reduction through multisensor fusion[C]. Proceedings of SPIE. USA:Orlando,1998:102-109.
[33]Bei Tang, Guillermo Sapiro, Vicent Caselles. Color Image Enhancement via Chromaticity Diffusion [J]. IEEE Transactions on Image Processing,2001, 10 (5):701-707.
[34]John P Oakley, Hong Bu. Correction of simple contrast loss in color images [J]. IEEE Transactions on Image Processing,2007,16 (2):511-522.
[35]沈垣,李舜酩,毛建国,辛江慧.数字图像复原技术综述[J].中国图象图形学报,2009,14(9)：1764-1775.
[36]薛弘晔,董俊.基于遗传算法的BP神经网络图像复原算法研究[J].中国科技信息,2009,1(2)：49-52.
[37]H. Koschmieder. Theorie der horizontalen sichtweite [J]. Beitr. Phys. Frei. Atmos.,1924,12:171-181.
[38]D. Sadot, N. S. Kopeika. Imaging through the atmosphere:practical instrumentation-based theory and verification of aerosol modulation transfer function [J]. JOSA A,1993,10 (1):172-179.
[39]Yitzhak Yitzhaky, Itai Dror, Norman S. Kopeika. Restoration of atmospherically blurred images according to weather-predicted atmospheric modulation transfer functions[J]. Opt. Eng.,1997,36 (11),3064-3072.
[40]John P. Oakley, Brenda L. Satherley. Improving Image Quality in Poor Visibility Conditions Using a Physical Moadel for Contrast Degradation [J]. IEEE TRANSACTION ON IMAGE PROCESSING,1998,7 (2):167-179.
[41]Narasimhan, S. G., Nayar, S. K. Chromatic framework for vision in bad weather [C]. In Proceedings of IEEE CVPR,2000:598--605.
[42]Srinivasa G. Narasimhan, Shree K.Nayar. removing weather effects from monochrome images[C]. Computer vision and pattern recognition,2001:186-193.
[43]Srinivasa G. Narasimhan, Shree K. Nayar. Vision and the atmosphere [J]. International J. Computer Vision,2002,48 (3):233-254.
[44]刘锦锋,黄峰.一种基于输运方程的大气退化图像复原方法[J].光散射学报,2005,16(4)：364369.
[45]钱徵,朱淼良.大气退化的自然山体图像复原研究[J].计算机辅助设计与图形学学报,2002,14(6)：526529.
[46]朱淼良,钱徵.自然景物中大气退化模型的研究[J].计算机辅助设计与图形学学报, 2001,13(9)：793-799.
[47]刘瑞,易萌,郑锦,李波.雾天景物成像的复原[C].中国计算机学会多媒体技术专业委员会.第二届和谐人机环境联合学术会议(HHME2006)第15届中国多媒体学术会议(NCMT06)论文集,中国：杭州,2006：375-380.
[48]Nicolas Hautiere, Jean-Philippe Tarel, Didier Aubert. Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration[C]. CVPR, USA: Minneapolis,2007:1-8.
[49]N. Hautiere, R. Labayrade, D. Aubert. Real-time disparity contrast combination for onboard estimation of the visibility distance[J]. IEEE Transactions on Intelligent Transportation Systems,2006,7 (2):201-212.
[50]Nicolas Hautiere, Jean-Philippe Tarel, Jean Lavenant, Didier Aubert. Automatic fog detection and estimation of visibility distance through use of an onboard camera. Mach[J]. Vis. Appl.,2006,17 (1):8-20.
[51]N. Hautiere, R. Labayrade, D. Aubert. Estimation of the visibility distance by stereovision:A generic approach [J]. IEICE Transactions on information and systems,2006,89 (7):2084-2091.
[52]Cl'ement Boussard, Nicolas Hautiere, Brigitte d'Andr'ea-Novel. Vehicle Dynamics Estimation for Camera-based Visibility Distance Estimation[C]. Intelligent Robots and systems, France:nice,2008:600-605.
[53]张瑞平.雾天条件下的图像的恢复研究[D].天津大学计算机科学与技术学院,2006年6月.
[54]S. G. Narasimhan and S. K. Nayar. Contrast restoration of weather degraded images. PAMI, (25):713-724,2003.
[55]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.
[56]董慧颖,方帅,王欣威,徐心和.基于物理模型的恶化天气下的图像复原方法及应用[J].东北大学学报(自然科学版),2005,26(3)：217-219.
[57]Jian Sun, Jiaya Jia, Chi-Keung Tang, Heung-Yeung Shum. Poisson matting[C]. ACM Transactions on Graphics,2004,23 (3):315-321.
[58]任俊,李志能,傅一平.薄雾天气下图像的复原与边缘检测研究[J].计算机辅助设计与图形学学报,2005,17(4)：694-698.
[59]葛君伟,谢祥华,方义秋.雾天图像清晰化方法及应用[J].重庆邮电大学学报(自然科学版),2010,22(2)：223-226.
[60]陈功,王唐,周荷琴.基于物理模型的雾天图像复原新方法[J].中国图象图形学报, 2005,13(5)：887-893.
[61]张秀琼,高志升,袁红照.基于物理模型的自动化图像去雾方法[J].仪器仪表学报,2008,29(4)：247251.
[62]Y. Schechner, Srinivasa G. Narasimhan, Shree K. Nayar. Instant dehazing of images using polarization [C]. Proc. IEEE CVPR, USA:Kauai,2001:325-332.
[63]Shwartz S, Namer E, Schechner Y Y. Blind haze separation[C]. Proceedings of the IEEE Computer Society Conferenceon Computer Vision and Pattern Recognition, USA:WashingtonD. C,2006:1984-1991.
[64]Sarit Shwartz, Einav Namer, Yoav Y. Schechner. Blind Haze Separation [C]. Proc. IEEE Computer Vision and Pattern Recognition, USA:NY,2006:1984-1991.
[65]王勇,薛模根,黄勤超.基于大气背景抑制的偏振去雾算法[J].计算机工程,2009,35(4)：271-175.
[66]邢秀娟,张永梅,邢秀琴.一种低能见度图像的盲分离复原算法[J].微电子学与计算机,2009,26(5)：152-155.
[67]周旋,周树道,黄峰,朱福萌.大气退化图像的处理研究[J].武汉大学学报(信息科学版),2005,30(8)：720-722.
[68]Daniela Smaranda Ionescu. foggy images processing by ICA method[J]. ACTA ELECTROTEHNICA,2005,46 (2):71-75
[69]Daniela Smaranda Ionescu. Foggy Images Enhancement by Estimation of the Model Parameters[J]. ACTA ELECTROTEHNICA,2005,46 (2):76-80
[70]Daniela Smaranda Ionescu, Mircea Curila, and Sorin Curila. Enhancing Visibility in Scattering Environment by BSS Technique[J]. ACTA ELECTROTEHNICA,2005,46 (1):29-38.
[71]R. Kaftory, Y. Y. Schechner, Y. Y. Zeevi. Variational distance-dependent image restoration [C]. IEEE CVPR, USA:Minneapolis,2007:1-8.
[72]Tali Treibitz, Yoav Y. Schechner. Recovery Limits in Pointwise Degradation[C]. Proc. IEEE ICCP, USA:San Francisco,2009:1-8.
[73]Schechner, Y. Y., Averbuch. Regularized image recovery in scattering media [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007, 29 (9):1655-1660.
[74]Tali Treibitz, Yoav Y. Schechner. Polarization:Beneficial for Visibility Enhancement?[C]. IEEE CVPR, USA:Miami,2009:1-8.
[75]Raanan Fattal. Single image dehazing [C]. International Conference on Computer Graphics and Interactive Techniques, USA:Los Angeles,2008:1-8.
[76]Robby T. Tan. Visibility in bad weather from a single image[C]. Computer Vision and Pattern Recognition, USA:Alaska, 2008: 1-8.
[77]Robby T. Tan, Niklas Pettersson, Lars Petersson. Visibility Enhancement of "oads with foggy or hazy scenes[C]. Proceedings of the2007 IEEE Intelligent Vehicles Symposium, Turkey:Istanbul,2007:13-15.
[78]R. T. Tan, K. Nishino, K. Ikeuchi. Color constancy through inverse intensity chromaticity space[J]. JOSA A.,2004,21 (3):321-334.
[79]Kaiming He, Jian Sun, Xiaoou Tang. Single Image Haze Removal using Dark Channel Prior [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA:AK,2009:1956-1963.
[80]孙玉宝,肖亮,韦志辉,吴慧中.基于篇微分方程的户外图像去雾方法[J].系统仿真学报,2007,19(16)：3739-3744.
[81]Peter Carr, Richard Hartley. Improved Single Image Dehazing using Geometry [C]. Digital Image Computing:Techniques and Applications. Australia:Melbourne, 2009:103-110.
[82]Chengming Zou, Jinrui Chen. Recovering Depth from a Single Image Using Dark Channel Prior[C].11th ACIS International Conference on Software Engineering, Artificial Intelligence, Network and Parallel/Distributed Computing, England:London,2009:93-96.
[83]Louis Kratz, Ko Nishino. Factorizing Scene Albedo and Depth from a Single Foggy Image[C].12th International Conference on Computer Vision (ICCV), Japan: Kyoto,2009:1701-1708.
[84]S.Bronte, L. M. Bergasa, P. F. Alcantarilla.Fog Detection System Based on Computer Vision Techniques[C]. Intelligent Transportation Systems, ITSC 09.12th International IEEE Conference, USA:St. Louis,2009:1-6.
[85]Johannes Kopf., B. Chen, M. Cohen, D. Cohen-Or,O. Deussen, M. Uyttendaele, D. Lischinski. etc. Deep Photo:Model-Based Photograph Enhancement and Viewing[J]. ACM Transactions on Graphics, Article 116,2008,27 (5):1-10.
[86]邹荣.最小失真图像去雾算法的改进[J].计算机工程,2009,35(21)：213215.
[87]J. S. Jaffe. Computer modelling and the design of optimal underwater imaging systems [J]. IEEE J. Oceanic Eng,1990,15(2):101-111.
[88]B. J. McGlamery. A computer model for underwater camera systems[J]. in Ocean Optics,1979,28:221-231.
[89]Liu Chao, Meng Wang. Removal of Water Scattering [C]. Computer Engineering and Technology, China:Chengdu,2010:1-5.
[90]T. Treibitz and Y. Y. Schechner. Active polarization descattering[J]. IEEE Trans. PAMT,2009,31 (3):385-399.
[91]Tali Treibitz and Yoav Y. Schechner, Instant 3Descatter[C], Proc. IEEE CVPR,USA:NY,2006:1861-1868.
[92]Yoav Y.Schechner, Nir Karpel. Clear Underwater Vision[C]. Proc. Computer Vision & Pattern Recognition, USA:Washington,2004:536-543.
[93]G. C. Giakos. Active backscattered optical polarimetric imaging of scattered targets[C]. In IEEE Instrument and Measurement Technology Conference (IMTC04), Italy:Como,2004:430-432.
[94]G. D. Gilbert, J. C. Pernicka. Improvement of underwater visibility by reduction of backscatter with a circular polarization technique [J]. App. Opt., 1967,6 (4):741-746.
[95]G. D. Lewis, D. L. Jordan, P. J. Roberts. Backscattering target detection in a turbid medium by polarization discrimination [J]. App. Opt.,1999,38 (18): 3937-3944.
[96]Nicholas Carlevaris-Bianco, Anush Mohan, Ryan M. Eustice. Initial Results in Underwater Single Image Dehazing [C]. OCEANS 2010 MTS, USA:Seattle,2010: 220-223.
[97]吴键.大气中的光传输理论[M].北京：北京邮电大学出版社,2005：11-20,34-55,99.
[98]Xu Renling. Particle Characterization:Light Scattering Methods[M]. NEW YORK: KLUWER ACADEMIC PUBLISHERS,2002:56-66,123-144,165.
[99]D. Toublanc. Henyey-Greenstein and Mie Phase Functions in Monte Carlo radiativ e transfer computations[J]. Appl. Opt.,1996,35 (18):3270-3274.
[100]S. K. Sharma, A. K. Roy. New Approximate Phase Functions:Test for Nonspherical Particles[J].J. Quant. Spectrosc. Radiat. Transfer,2000,64: 327-337.
[101]杨春平,吴健,万敏等.辐射传输方程中的平均单次散射反照率的计算[J],激光杂志,2007,28(4)：45-47.
[102]Craig F. Bohren, Donald R Huffman. Absorption and Scattering of Light by Small Particles[M]. USA:Wiley-Interscience,1998:150-180.
[103]Dave J V. Scattering of visible light by large water spheres [J]. Appl. Opt, 1969,8:155-161.
[104]Lentz W J. Generating bessel functions in Mie scattering calculation using continued fractions [J]. Appl. Opt, 1976, 15: 668-673.
[105]Wicombe W J. Improved Mie scattering algorithms [J]. Appl. Opt,1980,19: 1505-1509.
[106]余其铮,马国强.MIE散射算法的改进[J].哈尔滨工业大学学报,1987,1(3)：2026.
[107]朱震,叶茂,陆勇等.光散射粒度测量中Mie理论的高精度算法[J].光电子激光,1999，10(2)：135- 138.
[108]王式民,朱震,叶茂,等.光散射粒度测量中MIE理论两种改进的数值计算方法[J].计量学报,1999,20(4)：279-285.
[109]张合勇,赵卫疆,任德明,曲彦臣,宋宝安.球形粒子Mie散射参量的Matlab改进算法[J].光散射学报,2008,20(2)：102-110.
[110]李娟,江海鹰,牟向阳.Mie散射系数算法改进[J].济南大学学报(自然科学版),2009,23(4)：348350.
[111]V. D. HULST. Light scattering by small particles[M]. New York:Wiley,1957: 123-250.
[112]Chazette, Liousse. Acase study of optical and chemical ground apportionment for urban aerosols in Thessaloniki [J]. Atmospheric Environment, 2001, 35:2497-2506.
[113]Oleg Dubovik et al. Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations [J]. Journal of the atomosphereic sciences,2001,59:590-608.
[114]乔利锋.火灾烟颗粒偏振光散射特征的研究[D].合肥：中国科技大学,2008.
[115]王清华.光散射法颗粒大小与形状分析[D].南京：南京工业大学,2003.
[116]Katsumi Tadamura etc..Modeling of Skylight and Rendering of Outdoor Scenes [J]. Computer Graphics Forum,1993,12(3):189-200.
[117]Ishimaru A. Wave Propagation and Scattering in Random Media[M]. USA: Wiley-IEEE Press,1999:45-67,150.
[118]Earl J. McCartney. Optics of the atmosphere:Scattering by molecules and particles [M]. New York:John Wiley and Sons,1976:73-89.
[119]陈秀红,刘强,魏合理.多次散射辐射传输计算中的相函数处理[J].光散射学报,2007,19(3)：283-289.
[120]S. Chanderasekhar. Radiative Transfer[M].Oxford:The Clarendon Press,1950: 134-156.
[121]Srinivasa G. Narasimhan, Shree K. Nayar. Shedding light on the weather[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA:Wisconsin,2003:665-672.
[122]Arturo Quirantes.A T-matrix method and computer code for randomly oriented, axially symmetric coated scatterers [J]. Journal of Quantitative Spectroscopy &Radiative Transfer,2005,92 (3):373-381.
[123]李毅,潘功配,王雪.一级多次散射近似法求解烟幕透过率[J].火炸药学报,2002,2(2)：39-41.
[124]王园宇,李元宗,张天序.粉尘环境中的图像恢复方法[J].华中科技大学学报(自然科学版),2010,38(8)：42-48.
[125]Yuanyu Wang, Yuanzong Li, and Tianxu Zhang. The Method of Image Restoration in The Environments of Dust[C]. International Conference on Mechatronics and Automation, CHINA:XiAn,2010:294-298.
[126]林海洋,张忻.无参考图像质量评价综述[J].电脑知识与技术,2009,15(28)：80438046.
[127]周景超,戴汝为,肖柏华.图像质量评价研究综述[J].计算机科学,2008,35(7)：15.
[128]Hanghang Tong etc.. No Reference Quality Assessment for JPEG2000 Compressed Images[C]. International Conference on Image Processing, Singapore,2004: 3539-3542.
[129]F. M. Giaime Ginesu, D. Giusto. A multi-factors approach for image quality assessment based on a human visual system model[J]. Elsevier Signal Processing:Image Communication,2006,21 (4):316-333.
[130]Du Juan, Yu Yinglin, Xie Shengli. A new image quality assessment based on HVS [J]. Journal of Electronics (China),2005,22 (3):315-320.
[131]Wang Z, Cormack L, et. al. Laboratory for Image & Video Engineering. http://live.ece.utexas.edu/research/quality,/2002.
[132]Sheikh H R, Wang Z, Cormack L K, et. al. Blind quality assessment for JPEG2000 compressed images [C]. Thirty-Sixth Annual Asilomar Conference on Signals, Systems and Computers, USA:Monterey,2002:1735-1739.
[133]Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, etc.. Image Quality Assessment: From Error Visibility to Structural Similarity [J]. IEEE Transactions on Image Processing,2004,13 (4):600-612.
[134]Zhou Wang, Hamid R. Sheikh, Alan C. Bovik. No-Reference Perceptual Quality Assessment of JPEG Compressed Images [C]. International Conference on Image Processing, USA:New York,2002:477-480.
[135]Sheikh H R, Wang Z, Cormack L K, etc.. Blind quality assessment for JPEG2000 compressed images [C]. Thirty-Sixth Annual Asilomar Conference on Signals, Systems and Computers, USA:California,2002:1735-1739.
[136]H. R. Sheikh, A. C. Bovik, L. Cormack. No-Reference Quality Assessment Using Natural Scene Statistics:JPEG2000 [J]. IEEE Transactions on Image Processing,2005,14 (11):1918-1927.
[137]王园宇,李元宗,韩冀皖.自动基片分类机中的数字图像处理[J].机械工程与自动化,2009,5(5)：1-6.
[138]Gonzalez R, Woods R, Eddins S.数字图像处理(MATLAB版)[M].阮秋琦,译.北京：电子工业出版社,2004：3545.
[139]段瑞玲,李庆祥,李玉和.图像边缘检测方法和研究综述[J].光学技术,2005,31(3)：415-419.
[140]程彦,董守华,许永忠Snake算法边缘检测图像分析[J].中国医疗设备,2008,23(6)：49-51.
[141]伍尤富.图像处理中边缘检测研究方法[J].舰船电子工程,2006,26(4)：35-37.
[142]田莹,苑玮琦.遗传算法在图像处理中的应用[J].中国图象图形学报,2007,12(3)：389-396.
[143]王园宇,李元宗.基于GA-BP算法的基片图像边缘检测计算机应用[J].计算机工程与设计,2009,30(14)：3455-3457.
[144]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安：西安交通大学出版社,2002：815.
[145]周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999：11-31.
[146]玄光南,程润伟.遗传算法与工程设计[M].北京：科学出版社,2000：4-12.
[147]G. Wyszecki, W. S. Stiles. Color Science. Concepts and Methods, Quantitative Data and Formulae[M]. Second Edition, USA:John Wiley LSons,2000:234-256.
[148]赵晓丽,孙宪坤.基于视觉特性的彩色图像增强算法研究[J].计算机工程与设计,2009,30(19)：4458-4460.
[149]万丽,陈普春.一种基于数学形态学的图像对比度增强算法[J].现代电子技术,2009,32(13)：131-133.
[150]王惠,王宇,蔡光程.基于小波阈值的图像增强算法[J].电脑知识与技术,2007,(23)：1405-1408.
[151]朱孔凤,姜威,王端芳,张进,周贤.一种新的图像清晰度评价函数[J].红外与 激光工程,2005,34(4)：464-468.
[152]孙越,栾晓明,赵芳.一种改进的图像清晰度评价函数[J].应用科技,2009,36(9)：52-55.
[153]董晓坡.煤矿搜救机器人描述方法研究及其探测模块开发[D].四川：成都理工大学,2007.
[154]杜歆,朱云芳,刘济林.基于单目视觉的障碍物检测[J].浙江大学学报(工学版),2008,42(6)：913-917.
[155]杨建荣,曲仕茹.基于单目视觉的障碍物检测方法研究[J].计算机仿真,2009,26(2)：278-281
[156]Ulrich Ⅰ, Nourbakhsh Ⅰ. Appearance based obstacle detection with m onocular color vision [C]. Proceedings of the AAAI National Conference on Artificial Intelligence. USA:California,2000:866-871.
[157]刘振宇,姜楠,张令涛.基于人工路标和立体视觉的移动机器人自定位[J].计算机工程与应用,2010,46(9)：190-192.
[158]Ted Camus, David Coombs, Martin Herman, Tsai-Hong Hong.Real-time Single-workstation Obstacle Avoidance Using Only Wide-field Flow Divergence[C].13th International conference on Pattern RecognitionApplicat ions and Robotic Systems, Austria:Vienna,1996: 323-330.
[159]DeSouza G N, K ak A C. Vision for mobile robot navigation a survey [J]. IEEE Transactions on Pattern Analys is and Machine Intelligence,2002,24 (2): 237-267.
[160]TOM ASI C, KANADE T. Shape and motion from image streams under orthography: a factor izat ion method[J]. International Journal of Computer Vision,1992, 9(2):137-154.
[161]TRIGGS B. Factorization Methods for projective structure and motion [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. USA:Washington DC,1996:845-851.
[162]战强,吴佳.未知环境下移动机器人单目视觉导航算法[J].北京航空航天大学学报,2008,34(6)：613-617.
[163]沈志熙,黄瀚敏,黄席樾,李敏.基于视觉的室外移动机器人障碍物检测方法[J].机器人,2009,31(4)：304-310.
[164]郭磊,徐友春,李克强,连小珉.基于单目视觉的实时测距方法研究[J].中国图象图形学报,2006,11(1)：74-81.
[165]Andrzej Novwak,Andrzej Florek,Tomasz Piascik.Edge Tracing in a Priori Known Direction[C]. ECCV, Italy:Santa Margherita Ligure,1992:38-42.
[2]郭艳荣,鲜继清,吕霞付,谢正祥.基于视觉感知的最好质量图像评价方法[J].重庆科技学院学报(自然科学版),2009,11(6)：151-153.
[3]陈文兵,杨建伟,张小磊.能见度自动化计算方法[J].计算机工程与应用,2010,46(30)：181-183.
[4]Robby T. Tan. Visibility in BadWeather from a Single Image[C]. CVPR, USA: Anchorage,2008:1-8.
[5]郭璠,蔡自兴,谢斌,唐琎.图像去雾技术研究综述与展望[J].计算机应用,2010,30(9)：2417-2421.
[6]S. M. Pizer et al. Adaptive histogram equalization and itsvariations[J] Comput. Vis., Graph., Image Process.,1987,39:355-368.
[7]K. Zuiderveld. Contrast limited adaptive histogram equalization[C]. in Graphics Gems IV, P. Heckbert, Ed. USA:New York,1994:474-485.
[8]J. A. Stark. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEETrans. Image Process,2000,9(5):889-896.
[9]A. Polesel, G. Ramponi, V. J. Mathews. Image enhancement via adaptive unsharp masking[J]. IEEE Trans. Image Process.,2000,9 (3):505-510.
[10]王萍,张春,罗颖听.一种雾天图像低对比度增强的快速算法[J].计算机应用,2006,26(1)：152156.
[11]艾明晶,戴隆忠,曹庆华.雾天环境下自适应图像增强去雾方法研究[J].计算机仿真,2009,26(7)：244247.
[12]Y. S. Zhai, X. M. Liu. An improved fog-degraded image enhancement algorithm [C]. Wavelet Analysis and Pattern Recognition, China:Bejing,2007:522-526.
[13]詹翔,周焰.一种基于局部方差的雾天图像增强方法[J].计算机应用.2007,27(2)：510-512.
[14]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,2004,9(1)：124128.
[15]刘惠燕,何文章,马云飞.基于数学形态学的雾天图像增强算法[J].天津工程师范学院学报,2009,19(3)：34-35.
[16]Madasu Hanmandlu, Devendra Jha. An Optimal Fuzzy System for Color Image Enhancement[J]. IEEE Transactions on Image Processing,2006,15 (10): 2956-2966.
[17]翟艺书,柳晓鸣,涂雅瑗.基于模糊逻辑的雾天退化图像对比度增强算法[J].计算机应用,2008,28(3)：662-664.
[18]翟艺书,梁媛.基于混合对比度增强的户外图像去雾方法[J].计算机仿真,2010,27(5)：227-230.
[19]D. J. Jobson, Z. Rahman, G. A. Woodell. Properties and performance of a center/surround ret inex [J]. IEEE Trans. Image Process,1997,6(3):451-462.
[20]Daniel J. Jobson, Zia-ur Rahman, Glenn A. Woodell. A multi-scale retinex for bridging the gap between color images and the human observation of scenes [J]. IEEE Transaction on Image Process,1997,6 (7):965-976.
[21]Zia-ur. Rahman, Daniel. Jobson, Glenn A. Woodell. Retinex processing for automatic image enhancement [J]. Journal of Electronic Imaging,2004,13 (1): 100-110.
[22]李鹏.薄尘雾退化图像的处理研究[D]南京：南京理工大学,2005.
[23]刘瑞剑.低能见度条件下图像清晰化处理研究[D].太原：中北大学,2008.
[24]芮义斌,李鹏,孙锦涛.基于色彩恒常理论的图像去雾技术[J].南京理工大学学报,2006,30(5)：622-625.
[25]周树道,邵啸,朱涛,黄峰.薄雾影响下的退化彩色图像处理方法[J].解放军理工大学学报(自然科学版),2008,19(2)：142-145.
[26]P. Scheunders. A multivalued image wavelet representation based on multiscale fundamental forms [J]. IEEE Trans. Image Process,2002,10 (5):568-575.
[27]朱锡芳,徐芳芳. Retinex和小波变换去除遥感图像云雾方法分析[J].常州工学院学报,2007,20 (5)：8-13.
[28]Jean-Luc Starck, Fionn Murtagh, Emmanuel J. Candes, David L. Donoho. Gray and Color Image Contrast Enhancement by the Curvelet Transform [J]. IEEE Transactions on Image Processing.,2003,12 (6):706-717.
[29]L. Grewe, R. R. Brooks. Atmospheric attenuation reduction through multi-sensor fusion [C]. Proc. SPIE,1998,3376:102-109.
[30]Lex Schaul, Clement Fredembach, Sabinc Susstrunk. Color Image Dehazing Using The Near-Infrared [C].16th IEEE International Conference on Image Processing. Cairo:Egyp,2009:1629-1632.
[31]M. Wilscy, Jisha John. A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images [C]. Proceedings of the World Congress on Engineering. U.K:London,2008:705-709.
[32]Grewe.Lynne L.; Brooks. Richard R. Atmospheric attenuation reduction through multisensor fusion[C]. Proceedings of SPIE. USA:Orlando,1998:102-109.
[33]Bei Tang, Guillermo Sapiro, Vicent Caselles. Color Image Enhancement via Chromaticity Diffusion [J]. IEEE Transactions on Image Processing,2001, 10 (5):701-707.
[34]John P Oakley, Hong Bu. Correction of simple contrast loss in color images [J]. IEEE Transactions on Image Processing,2007,16 (2):511-522.
[35]沈垣,李舜酩,毛建国,辛江慧.数字图像复原技术综述[J].中国图象图形学报,2009,14(9)：1764-1775.
[36]薛弘晔,董俊.基于遗传算法的BP神经网络图像复原算法研究[J].中国科技信息,2009,1(2)：49-52.
[37]H. Koschmieder. Theorie der horizontalen sichtweite [J]. Beitr. Phys. Frei. Atmos.,1924,12:171-181.
[38]D. Sadot, N. S. Kopeika. Imaging through the atmosphere:practical instrumentation-based theory and verification of aerosol modulation transfer function [J]. JOSA A,1993,10 (1):172-179.
[39]Yitzhak Yitzhaky, Itai Dror, Norman S. Kopeika. Restoration of atmospherically blurred images according to weather-predicted atmospheric modulation transfer functions[J]. Opt. Eng.,1997,36 (11),3064-3072.
[40]John P. Oakley, Brenda L. Satherley. Improving Image Quality in Poor Visibility Conditions Using a Physical Moadel for Contrast Degradation [J]. IEEE TRANSACTION ON IMAGE PROCESSING,1998,7 (2):167-179.
[41]Narasimhan, S. G., Nayar, S. K. Chromatic framework for vision in bad weather [C]. In Proceedings of IEEE CVPR,2000:598--605.
[42]Srinivasa G. Narasimhan, Shree K.Nayar. removing weather effects from monochrome images[C]. Computer vision and pattern recognition,2001:186-193.
[43]Srinivasa G. Narasimhan, Shree K. Nayar. Vision and the atmosphere [J]. International J. Computer Vision,2002,48 (3):233-254.
[44]刘锦锋,黄峰.一种基于输运方程的大气退化图像复原方法[J].光散射学报,2005,16(4)：364369.
[45]钱徵,朱淼良.大气退化的自然山体图像复原研究[J].计算机辅助设计与图形学学报,2002,14(6)：526529.
[46]朱淼良,钱徵.自然景物中大气退化模型的研究[J].计算机辅助设计与图形学学报, 2001,13(9)：793-799.
[47]刘瑞,易萌,郑锦,李波.雾天景物成像的复原[C].中国计算机学会多媒体技术专业委员会.第二届和谐人机环境联合学术会议(HHME2006)第15届中国多媒体学术会议(NCMT06)论文集,中国：杭州,2006：375-380.
[48]Nicolas Hautiere, Jean-Philippe Tarel, Didier Aubert. Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration[C]. CVPR, USA: Minneapolis,2007:1-8.
[49]N. Hautiere, R. Labayrade, D. Aubert. Real-time disparity contrast combination for onboard estimation of the visibility distance[J]. IEEE Transactions on Intelligent Transportation Systems,2006,7 (2):201-212.
[50]Nicolas Hautiere, Jean-Philippe Tarel, Jean Lavenant, Didier Aubert. Automatic fog detection and estimation of visibility distance through use of an onboard camera. Mach[J]. Vis. Appl.,2006,17 (1):8-20.
[51]N. Hautiere, R. Labayrade, D. Aubert. Estimation of the visibility distance by stereovision:A generic approach [J]. IEICE Transactions on information and systems,2006,89 (7):2084-2091.
[52]Cl'ement Boussard, Nicolas Hautiere, Brigitte d'Andr'ea-Novel. Vehicle Dynamics Estimation for Camera-based Visibility Distance Estimation[C]. Intelligent Robots and systems, France:nice,2008:600-605.
[53]张瑞平.雾天条件下的图像的恢复研究[D].天津大学计算机科学与技术学院,2006年6月.
[54]S. G. Narasimhan and S. K. Nayar. Contrast restoration of weather degraded images. PAMI, (25):713-724,2003.
[55]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.
[56]董慧颖,方帅,王欣威,徐心和.基于物理模型的恶化天气下的图像复原方法及应用[J].东北大学学报(自然科学版),2005,26(3)：217-219.
[57]Jian Sun, Jiaya Jia, Chi-Keung Tang, Heung-Yeung Shum. Poisson matting[C]. ACM Transactions on Graphics,2004,23 (3):315-321.
[58]任俊,李志能,傅一平.薄雾天气下图像的复原与边缘检测研究[J].计算机辅助设计与图形学学报,2005,17(4)：694-698.
[59]葛君伟,谢祥华,方义秋.雾天图像清晰化方法及应用[J].重庆邮电大学学报(自然科学版),2010,22(2)：223-226.
[60]陈功,王唐,周荷琴.基于物理模型的雾天图像复原新方法[J].中国图象图形学报, 2005,13(5)：887-893.
[61]张秀琼,高志升,袁红照.基于物理模型的自动化图像去雾方法[J].仪器仪表学报,2008,29(4)：247251.
[62]Y. Schechner, Srinivasa G. Narasimhan, Shree K. Nayar. Instant dehazing of images using polarization [C]. Proc. IEEE CVPR, USA:Kauai,2001:325-332.
[63]Shwartz S, Namer E, Schechner Y Y. Blind haze separation[C]. Proceedings of the IEEE Computer Society Conferenceon Computer Vision and Pattern Recognition, USA:WashingtonD. C,2006:1984-1991.
[64]Sarit Shwartz, Einav Namer, Yoav Y. Schechner. Blind Haze Separation [C]. Proc. IEEE Computer Vision and Pattern Recognition, USA:NY,2006:1984-1991.
[65]王勇,薛模根,黄勤超.基于大气背景抑制的偏振去雾算法[J].计算机工程,2009,35(4)：271-175.
[66]邢秀娟,张永梅,邢秀琴.一种低能见度图像的盲分离复原算法[J].微电子学与计算机,2009,26(5)：152-155.
[67]周旋,周树道,黄峰,朱福萌.大气退化图像的处理研究[J].武汉大学学报(信息科学版),2005,30(8)：720-722.
[68]Daniela Smaranda Ionescu. foggy images processing by ICA method[J]. ACTA ELECTROTEHNICA,2005,46 (2):71-75
[69]Daniela Smaranda Ionescu. Foggy Images Enhancement by Estimation of the Model Parameters[J]. ACTA ELECTROTEHNICA,2005,46 (2):76-80
[70]Daniela Smaranda Ionescu, Mircea Curila, and Sorin Curila. Enhancing Visibility in Scattering Environment by BSS Technique[J]. ACTA ELECTROTEHNICA,2005,46 (1):29-38.
[71]R. Kaftory, Y. Y. Schechner, Y. Y. Zeevi. Variational distance-dependent image restoration [C]. IEEE CVPR, USA:Minneapolis,2007:1-8.
[72]Tali Treibitz, Yoav Y. Schechner. Recovery Limits in Pointwise Degradation[C]. Proc. IEEE ICCP, USA:San Francisco,2009:1-8.
[73]Schechner, Y. Y., Averbuch. Regularized image recovery in scattering media [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007, 29 (9):1655-1660.
[74]Tali Treibitz, Yoav Y. Schechner. Polarization:Beneficial for Visibility Enhancement?[C]. IEEE CVPR, USA:Miami,2009:1-8.
[75]Raanan Fattal. Single image dehazing [C]. International Conference on Computer Graphics and Interactive Techniques, USA:Los Angeles,2008:1-8.
[76]Robby T. Tan. Visibility in bad weather from a single image[C]. Computer Vision and Pattern Recognition, USA:Alaska, 2008: 1-8.
[77]Robby T. Tan, Niklas Pettersson, Lars Petersson. Visibility Enhancement of "oads with foggy or hazy scenes[C]. Proceedings of the2007 IEEE Intelligent Vehicles Symposium, Turkey:Istanbul,2007:13-15.
[78]R. T. Tan, K. Nishino, K. Ikeuchi. Color constancy through inverse intensity chromaticity space[J]. JOSA A.,2004,21 (3):321-334.
[79]Kaiming He, Jian Sun, Xiaoou Tang. Single Image Haze Removal using Dark Channel Prior [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA:AK,2009:1956-1963.
[80]孙玉宝,肖亮,韦志辉,吴慧中.基于篇微分方程的户外图像去雾方法[J].系统仿真学报,2007,19(16)：3739-3744.
[81]Peter Carr, Richard Hartley. Improved Single Image Dehazing using Geometry [C]. Digital Image Computing:Techniques and Applications. Australia:Melbourne, 2009:103-110.
[82]Chengming Zou, Jinrui Chen. Recovering Depth from a Single Image Using Dark Channel Prior[C].11th ACIS International Conference on Software Engineering, Artificial Intelligence, Network and Parallel/Distributed Computing, England:London,2009:93-96.
[83]Louis Kratz, Ko Nishino. Factorizing Scene Albedo and Depth from a Single Foggy Image[C].12th International Conference on Computer Vision (ICCV), Japan: Kyoto,2009:1701-1708.
[84]S.Bronte, L. M. Bergasa, P. F. Alcantarilla.Fog Detection System Based on Computer Vision Techniques[C]. Intelligent Transportation Systems, ITSC 09.12th International IEEE Conference, USA:St. Louis,2009:1-6.
[85]Johannes Kopf., B. Chen, M. Cohen, D. Cohen-Or,O. Deussen, M. Uyttendaele, D. Lischinski. etc. Deep Photo:Model-Based Photograph Enhancement and Viewing[J]. ACM Transactions on Graphics, Article 116,2008,27 (5):1-10.
[86]邹荣.最小失真图像去雾算法的改进[J].计算机工程,2009,35(21)：213215.
[87]J. S. Jaffe. Computer modelling and the design of optimal underwater imaging systems [J]. IEEE J. Oceanic Eng,1990,15(2):101-111.
[88]B. J. McGlamery. A computer model for underwater camera systems[J]. in Ocean Optics,1979,28:221-231.
[89]Liu Chao, Meng Wang. Removal of Water Scattering [C]. Computer Engineering and Technology, China:Chengdu,2010:1-5.
[90]T. Treibitz and Y. Y. Schechner. Active polarization descattering[J]. IEEE Trans. PAMT,2009,31 (3):385-399.
[91]Tali Treibitz and Yoav Y. Schechner, Instant 3Descatter[C], Proc. IEEE CVPR,USA:NY,2006:1861-1868.
[92]Yoav Y.Schechner, Nir Karpel. Clear Underwater Vision[C]. Proc. Computer Vision & Pattern Recognition, USA:Washington,2004:536-543.
[93]G. C. Giakos. Active backscattered optical polarimetric imaging of scattered targets[C]. In IEEE Instrument and Measurement Technology Conference (IMTC04), Italy:Como,2004:430-432.
[94]G. D. Gilbert, J. C. Pernicka. Improvement of underwater visibility by reduction of backscatter with a circular polarization technique [J]. App. Opt., 1967,6 (4):741-746.
[95]G. D. Lewis, D. L. Jordan, P. J. Roberts. Backscattering target detection in a turbid medium by polarization discrimination [J]. App. Opt.,1999,38 (18): 3937-3944.
[96]Nicholas Carlevaris-Bianco, Anush Mohan, Ryan M. Eustice. Initial Results in Underwater Single Image Dehazing [C]. OCEANS 2010 MTS, USA:Seattle,2010: 220-223.
[97]吴键.大气中的光传输理论[M].北京：北京邮电大学出版社,2005：11-20,34-55,99.
[98]Xu Renling. Particle Characterization:Light Scattering Methods[M]. NEW YORK: KLUWER ACADEMIC PUBLISHERS,2002:56-66,123-144,165.
[99]D. Toublanc. Henyey-Greenstein and Mie Phase Functions in Monte Carlo radiativ e transfer computations[J]. Appl. Opt.,1996,35 (18):3270-3274.
[100]S. K. Sharma, A. K. Roy. New Approximate Phase Functions:Test for Nonspherical Particles[J].J. Quant. Spectrosc. Radiat. Transfer,2000,64: 327-337.
[101]杨春平,吴健,万敏等.辐射传输方程中的平均单次散射反照率的计算[J],激光杂志,2007,28(4)：45-47.
[102]Craig F. Bohren, Donald R Huffman. Absorption and Scattering of Light by Small Particles[M]. USA:Wiley-Interscience,1998:150-180.
[103]Dave J V. Scattering of visible light by large water spheres [J]. Appl. Opt, 1969,8:155-161.
[104]Lentz W J. Generating bessel functions in Mie scattering calculation using continued fractions [J]. Appl. Opt, 1976, 15: 668-673.
[105]Wicombe W J. Improved Mie scattering algorithms [J]. Appl. Opt,1980,19: 1505-1509.
[106]余其铮,马国强.MIE散射算法的改进[J].哈尔滨工业大学学报,1987,1(3)：2026.
[107]朱震,叶茂,陆勇等.光散射粒度测量中Mie理论的高精度算法[J].光电子激光,1999，10(2)：135- 138.
[108]王式民,朱震,叶茂,等.光散射粒度测量中MIE理论两种改进的数值计算方法[J].计量学报,1999,20(4)：279-285.
[109]张合勇,赵卫疆,任德明,曲彦臣,宋宝安.球形粒子Mie散射参量的Matlab改进算法[J].光散射学报,2008,20(2)：102-110.
[110]李娟,江海鹰,牟向阳.Mie散射系数算法改进[J].济南大学学报(自然科学版),2009,23(4)：348350.
[111]V. D. HULST. Light scattering by small particles[M]. New York:Wiley,1957: 123-250.
[112]Chazette, Liousse. Acase study of optical and chemical ground apportionment for urban aerosols in Thessaloniki [J]. Atmospheric Environment, 2001, 35:2497-2506.
[113]Oleg Dubovik et al. Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations [J]. Journal of the atomosphereic sciences,2001,59:590-608.
[114]乔利锋.火灾烟颗粒偏振光散射特征的研究[D].合肥：中国科技大学,2008.
[115]王清华.光散射法颗粒大小与形状分析[D].南京：南京工业大学,2003.
[116]Katsumi Tadamura etc..Modeling of Skylight and Rendering of Outdoor Scenes [J]. Computer Graphics Forum,1993,12(3):189-200.
[117]Ishimaru A. Wave Propagation and Scattering in Random Media[M]. USA: Wiley-IEEE Press,1999:45-67,150.
[118]Earl J. McCartney. Optics of the atmosphere:Scattering by molecules and particles [M]. New York:John Wiley and Sons,1976:73-89.
[119]陈秀红,刘强,魏合理.多次散射辐射传输计算中的相函数处理[J].光散射学报,2007,19(3)：283-289.
[120]S. Chanderasekhar. Radiative Transfer[M].Oxford:The Clarendon Press,1950: 134-156.
[121]Srinivasa G. Narasimhan, Shree K. Nayar. Shedding light on the weather[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA:Wisconsin,2003:665-672.
[122]Arturo Quirantes.A T-matrix method and computer code for randomly oriented, axially symmetric coated scatterers [J]. Journal of Quantitative Spectroscopy &Radiative Transfer,2005,92 (3):373-381.
[123]李毅,潘功配,王雪.一级多次散射近似法求解烟幕透过率[J].火炸药学报,2002,2(2)：39-41.
[124]王园宇,李元宗,张天序.粉尘环境中的图像恢复方法[J].华中科技大学学报(自然科学版),2010,38(8)：42-48.
[125]Yuanyu Wang, Yuanzong Li, and Tianxu Zhang. The Method of Image Restoration in The Environments of Dust[C]. International Conference on Mechatronics and Automation, CHINA:XiAn,2010:294-298.
[126]林海洋,张忻.无参考图像质量评价综述[J].电脑知识与技术,2009,15(28)：80438046.
[127]周景超,戴汝为,肖柏华.图像质量评价研究综述[J].计算机科学,2008,35(7)：15.
[128]Hanghang Tong etc.. No Reference Quality Assessment for JPEG2000 Compressed Images[C]. International Conference on Image Processing, Singapore,2004: 3539-3542.
[129]F. M. Giaime Ginesu, D. Giusto. A multi-factors approach for image quality assessment based on a human visual system model[J]. Elsevier Signal Processing:Image Communication,2006,21 (4):316-333.
[130]Du Juan, Yu Yinglin, Xie Shengli. A new image quality assessment based on HVS [J]. Journal of Electronics (China),2005,22 (3):315-320.
[131]Wang Z, Cormack L, et. al. Laboratory for Image & Video Engineering. http://live.ece.utexas.edu/research/quality,/2002.
[132]Sheikh H R, Wang Z, Cormack L K, et. al. Blind quality assessment for JPEG2000 compressed images [C]. Thirty-Sixth Annual Asilomar Conference on Signals, Systems and Computers, USA:Monterey,2002:1735-1739.
[133]Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, etc.. Image Quality Assessment: From Error Visibility to Structural Similarity [J]. IEEE Transactions on Image Processing,2004,13 (4):600-612.
[134]Zhou Wang, Hamid R. Sheikh, Alan C. Bovik. No-Reference Perceptual Quality Assessment of JPEG Compressed Images [C]. International Conference on Image Processing, USA:New York,2002:477-480.
[135]Sheikh H R, Wang Z, Cormack L K, etc.. Blind quality assessment for JPEG2000 compressed images [C]. Thirty-Sixth Annual Asilomar Conference on Signals, Systems and Computers, USA:California,2002:1735-1739.
[136]H. R. Sheikh, A. C. Bovik, L. Cormack. No-Reference Quality Assessment Using Natural Scene Statistics:JPEG2000 [J]. IEEE Transactions on Image Processing,2005,14 (11):1918-1927.
[137]王园宇,李元宗,韩冀皖.自动基片分类机中的数字图像处理[J].机械工程与自动化,2009,5(5)：1-6.
[138]Gonzalez R, Woods R, Eddins S.数字图像处理(MATLAB版)[M].阮秋琦,译.北京：电子工业出版社,2004：3545.
[139]段瑞玲,李庆祥,李玉和.图像边缘检测方法和研究综述[J].光学技术,2005,31(3)：415-419.
[140]程彦,董守华,许永忠Snake算法边缘检测图像分析[J].中国医疗设备,2008,23(6)：49-51.
[141]伍尤富.图像处理中边缘检测研究方法[J].舰船电子工程,2006,26(4)：35-37.
[142]田莹,苑玮琦.遗传算法在图像处理中的应用[J].中国图象图形学报,2007,12(3)：389-396.
[143]王园宇,李元宗.基于GA-BP算法的基片图像边缘检测计算机应用[J].计算机工程与设计,2009,30(14)：3455-3457.
[144]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安：西安交通大学出版社,2002：815.
[145]周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999：11-31.
[146]玄光南,程润伟.遗传算法与工程设计[M].北京：科学出版社,2000：4-12.
[147]G. Wyszecki, W. S. Stiles. Color Science. Concepts and Methods, Quantitative Data and Formulae[M]. Second Edition, USA:John Wiley LSons,2000:234-256.
[148]赵晓丽,孙宪坤.基于视觉特性的彩色图像增强算法研究[J].计算机工程与设计,2009,30(19)：4458-4460.
[149]万丽,陈普春.一种基于数学形态学的图像对比度增强算法[J].现代电子技术,2009,32(13)：131-133.
[150]王惠,王宇,蔡光程.基于小波阈值的图像增强算法[J].电脑知识与技术,2007,(23)：1405-1408.
[151]朱孔凤,姜威,王端芳,张进,周贤.一种新的图像清晰度评价函数[J].红外与 激光工程,2005,34(4)：464-468.
[152]孙越,栾晓明,赵芳.一种改进的图像清晰度评价函数[J].应用科技,2009,36(9)：52-55.
[153]董晓坡.煤矿搜救机器人描述方法研究及其探测模块开发[D].四川：成都理工大学,2007.
[154]杜歆,朱云芳,刘济林.基于单目视觉的障碍物检测[J].浙江大学学报(工学版),2008,42(6)：913-917.
[155]杨建荣,曲仕茹.基于单目视觉的障碍物检测方法研究[J].计算机仿真,2009,26(2)：278-281
[156]Ulrich Ⅰ, Nourbakhsh Ⅰ. Appearance based obstacle detection with m onocular color vision [C]. Proceedings of the AAAI National Conference on Artificial Intelligence. USA:California,2000:866-871.
[157]刘振宇,姜楠,张令涛.基于人工路标和立体视觉的移动机器人自定位[J].计算机工程与应用,2010,46(9)：190-192.
[158]Ted Camus, David Coombs, Martin Herman, Tsai-Hong Hong.Real-time Single-workstation Obstacle Avoidance Using Only Wide-field Flow Divergence[C].13th International conference on Pattern RecognitionApplicat ions and Robotic Systems, Austria:Vienna,1996: 323-330.
[159]DeSouza G N, K ak A C. Vision for mobile robot navigation a survey [J]. IEEE Transactions on Pattern Analys is and Machine Intelligence,2002,24 (2): 237-267.
[160]TOM ASI C, KANADE T. Shape and motion from image streams under orthography: a factor izat ion method[J]. International Journal of Computer Vision,1992, 9(2):137-154.
[161]TRIGGS B. Factorization Methods for projective structure and motion [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. USA:Washington DC,1996:845-851.
[162]战强,吴佳.未知环境下移动机器人单目视觉导航算法[J].北京航空航天大学学报,2008,34(6)：613-617.
[163]沈志熙,黄瀚敏,黄席樾,李敏.基于视觉的室外移动机器人障碍物检测方法[J].机器人,2009,31(4)：304-310.
[164]郭磊,徐友春,李克强,连小珉.基于单目视觉的实时测距方法研究[J].中国图象图形学报,2006,11(1)：74-81.
[165]Andrzej Novwak,Andrzej Florek,Tomasz Piascik.Edge Tracing in a Priori Known Direction[C]. ECCV, Italy:Santa Margherita Ligure,1992:38-42.