基于RETINEX理论的视频图像增强系统研究
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摘要
充分运用大气光学的理论知识,研究了夜晚和不同恶劣天气条件下引起视频图像退化的成因机理,归纳了夜晚和大雾、大雨、沙尘等恶劣天气条件下图像退化的特征。总结和分析了采用基于退化模型的图像恢复方法存在的难度和不足,以及采用图像增强方法的优点。详细讨论了目前常用的图像增强技术在处理夜晚和不同恶劣天气条件下退化图像时存在的问题或不足,研究了基于Retinex理论的图像增强方法的特点。指出利用Retinex理论克服不同条件下由于光照影响造成图像退化的可行性,并基于Retinex理论提出适合夜晚和大雾、大雨、沙尘等恶劣天气条件下退化图像的增强方法。通过优化本文提出的图像增强方法,基于DSP平台,研制了一种新型视频图像增强系统,可以完善和提高原有的视频监控系统。
The cause and mechanism of degradation of video images captured in night and bad weathers are studied by making full use of the theory of the atmosphere optics. The characteristics of images captured in night, fog, rain and dusty weather are concluded. The difficulties and insufficiencies of image restoration techniques based on the degradation models and the merits of image enhancement techniques are summarized and analyzed. The insufficiencies of current widely used image enhancement techniques are discussed in detail when these techniques are used to process night images and bad weathers degraded images and the characteristics of image enhancement techniques based on Retinex theory are researched. The feasibility of applying Retinex theory to overcome degradation by different illumination is pointed out and two enhancement methods based on Retinex theory respectively suit for night images and images captured in bad weathers, such as fog, rain and dust, are proposed in this paper. A real-time video images enhancement system based on DSP paltform is developed by optimizing the proposed image enhancement methods, which will perfect and enhance original video surveillance system.
The cause and mechanism of degradation of video images captured in night and bad weathers are studied by making full use of the theory of the atmosphere optics. The characteristics of images captured in night, fog, rain and dusty weather are concluded. The difficulties and insufficiencies of image restoration techniques based on the degradation models and the merits of image enhancement techniques are summarized and analyzed. The insufficiencies of current widely used image enhancement techniques are discussed in detail when these techniques are used to process night images and bad weathers degraded images and the characteristics of image enhancement techniques based on Retinex theory are researched. The feasibility of applying Retinex theory to overcome degradation by different illumination is pointed out and two enhancement methods based on Retinex theory respectively suit for night images and images captured in bad weathers, such as fog, rain and dust, are proposed in this paper. A real-time video images enhancement system based on DSP paltform is developed by optimizing the proposed image enhancement methods, which will perfect and enhance original video surveillance system.
引文
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7. Kok keong Tan, John P. Oakley. Enhancement of color images in poor visibility conditions. International Conference on Image Processing,2000,2:30～33
8. Srinivasa G. Narasimhan. Shree K. Nayar. Chromatic framework for vision in bad weather. Proceedings of IEEE Conference on Computer Vision and Patern Recognition,2000,1: 598～605
9. M. J. Rakovic, G. W. Kattawar, M. Mehrubeoglu. Light backscattering polarization patterns from turbid media:theory and experiment. Applied Optics,1999.38(15):3399～3408
10. Yoav Y. Schechner, Srinivasa G. Narasimhan, Shree K. Nayar. Instant dehazing of images using polarization. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2001,1:1325-1332
11. Yoav Y. Schechner, Srinivasa G. Narasimhan, Shree K. Nayar. Polarization based vision through haze. Applied Opitics,2003,42(3):511～525
12. Srinivasa G. Narasimhan, Shree K. Nayar. Removing weather effects from monochrome images. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2001,2:186～193
13. Srinivasa G. Narasimhan, Shree K. Nayar. Interactive (de)weathering of an image using physical models. International Conference on Computer Graphics and Interactive Techniques, 2008
14.武风霞,王章野,彭群生.最小失真意义下雾化图像复原.系统仿真学报，2006,18(1)：363～365
15. Richard A. Young. The Gaussian derivative model for spatial vision:I. Retinal mechanisms. 1987,2(4):273～293
16. Fried D L. Optical resolution through a randomly inhomogeneous medium for very long and very short exposures. Journal of the Optical Society of America.1966,56(10):1372～1379
17.祝培，朱虹，钱学明.一种有雾天气图像景物影像的清晰化方法.中国图象图形学报,2004,9(1)：124～128
18.艾明晶,戴隆忠,曹庆华.雾天环境下自适应图像增强云雾方法研究.计算机仿真，2009, (7)：244～247
19.王萍,张春,罗颖昕.一种雾天图像低对比度增强的快速算法.计算机应用,2006,26(1)：152～154
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