【作者】 陈先桥；

【导师】 严新平；

【作者基本信息】 武汉理工大学 ， 交通信息工程及控制， 2008， 博士

【摘要】 雾是常见的一种自然现象,即使是晴朗的夏天,由于地面水气的蒸发,也会有薄雾的产生。雾又是一种灾害性天气,被国际上列为十大灾害天气之一,它对城乡公路运输、航空航海、电力系统、工农业生产以及人们的日常生活乃至身体健康均有不同程度影响。雾对道路交通运输的影响最为严重,它在道路交通运输中形成严重的视程障碍,是造成交通事故的重要原因之一。因此,研究如何提高雾天等恶劣天气条件下道路环境系统的可视性、预防低能见度天气条件下恶性道路交通事故的发生一直是交通与信息领域的一个研究热点。国内外科技工作者已对该领域从不同的角度进行了广泛的研究,并且取得了许多阶段性的研究成果。但由于雾天退化图像的致因复杂,采集的信息严重不足,现有的算法和退化模型都不能充分准确地描述图像退化的根本原因,视觉改善的效果与实际需要还有很大的距离。因此,研究如何对雾等恶劣天气条件下获得的退化图像进行有效的处理具有非常重要的理论和实际意义。本文对雾天条件下影响驾驶行为的相关对象从各方面分析了其特性,研究并改进了有关的增强算法或复原模型。具体完成的主要工作如下:(1)分析了雾天交通场景中相关对象的色彩、频谱等特性,提取了其色彩、纹理的各种统计特征。在提取颜色特征的过程中,根据各对象在H、S、L子空间的直方图分布特性,选取了色彩均值和标准差特征。在提取纹理特征过程中,根据各相关对象的频谱分布、能量分布、抽象维数变化特性,提取了包括中心低频能量、水平方向低频能量、垂直方向低频能量比、不同尺度下各子空间的能量均值和标准差、分形维数等纹量特征。(2)提出了基于RETINEX理论和天空区域自动分离的雾天交通场景图像复原方法。在天空区域自动分离处理中,将带雾图像划分为子块,将问题转化为每一块归属为天空区域和非天空区域的不确定性分类问题,并引入EM方法实现天空区域的自动分离。针对EM方法要求已知各类内服从某种分布,其应用受到一定限制的特点,讨论了将FCM方法改进并移植到天空区域的自动分离之中。针对不同特征分量的组合比较了以上两种方法的分离效果,并将PCA方法引入选取主要特征分量得到近似最优的分类效果。在成功实现天空区域的自动分离后,研究了利用天空区域逼近光照图像的方法,实现利用RETINEX理论改善雾天交通场景中降质图像的视觉效果。(3)在利用大气退化模型实现带雾图像的增强过程中,提出了两种改进的模型参数估计方法。在利用单幅图像估计大气退化模型参数过程中,利用简单方法初步估计出模型中参数的近似值,并将其代入大气退化模型中,得到景物图像I的初步估计值。然后,再利用I的初步估计值和原始图像E优化参数,多次迭代实现较好的复原效果;在利用同一场景不同雾浓度下的多图像估计模型参数处理中,通过提取等深线常量特征和定义类内类间距离,改进并引入FCM方法实现等深线的自动分类。然后通过两个不同雾浓度图像的比较在不需要明确估计出景物点深度的情况下实现利用大气退化模型增强处理雾天交通场景中的降质图像。(4)在利用变分模型实现带雾图像增强过程中,提出了两种基于大气退化型或已知景深分布情况下的变分模型。第一种模型是以反映对比度变化的全变分极小化模型为基础,增加大气退化模型的约束条件,建立了基于对比度拉伸和大气退化方程约束的增强模型。第二种模型是以全变分模型对噪声的抑制和对比度场变分对纹理的增强,增加一个相似性要求,保证输出图像与大气退化模型复原图像的一致性相结合作为变分模型,共同建立一个变分求解问题。(5)提出了若干雾天图像增强中的快速算法。第一,在分析了使用较广泛的传统直方图均衡化方法,提出了改进的直方图均衡化方法。基本思路是在天空区域增强幅度压缩。估计图像中各景物点的景深,在景物点处的拉伸幅度与景深距离近似成正比关系。第二,考虑景深距离补偿的快速图像增强算法。在设计雾天交通场景图像增强算法时,既保留了直方图均衡化方法对局部信息量集中处对比度拉伸的优点,同时又考虑了天空区域的独特性和景物点随景深增加而衰减的规律。第三,对SSR算法进行了简化,提出了一种改进的SSR快速算法。传统的SSR算法,利用尺度函数构造图像的照度函数实现雾天图像的增强。但在应用尺度函数构造照度函数的过程中存在大量的卷积运算,计算量较大。本文给出一种简化的利用尺度函数逼近照度函数的快速算法。更多还原

【Abstract】 Fog is a weather phenomenon.Even on a sunnyday,we can see that an object at a distance is affected by fog.Fog,which is caused by water vapor,is also a problematic weather,and is listed in top ten problematic weathers.It causes many bad results to traffic,aviation,traveling,our daily lives and our health.For the military,foggy images might bring distortion that can result in serious event.Traffic is affected by weather in a most serious way.The driver’s view is wreaked,and in many cases it will result in a traffic accident.So how to improve a driver’s view in foggy weather has become the focuse of academics in information and ITS science.Many scientist and engineers have studied it a lot,and many great achievements have been made so far.But the problem is very difficult and complicated and the information about objects in foggy traffic is insufficient.Currently the degraded process of a foggy image can not be described by any algorithms or models perfectly. There is still much works to do on this research subject,so it has a great significance to study how to enhance the foggy traffic images.In this thesis,we analyzed the features of objects in foggy traffic and the algorithm and models which are used to enhance the degraded images.The main works completed are as follows:(1) Analyzed the color and spectrum features of objects in foggy traffic, extracted their characteristics by histogram,spastics methods.With the analyzing of spectrum,energy,abstract dimensions,we got many useful traits or characteristics, such as center,horizontal,and vertical low frequency energy,multi-scale sub-space energy distribution,and fractal dimensions.(2) Proposed one restoration algorithm for foggy traffic images by the combination of the Retinex theory and the sky area.The sky area was detached from the traffic images by EM or FCM.In one case,the PCA was used to get a nearly perfect result.After detaching the sky area successfully,the illumination was reconstructed by the information of the sky areas.(3) Proposed two methods which can be used to estimate the parameters in the atmospheric scattering model.The first one is the improved algorithm using one foggy image.First,we get the rough estimations of the object’s images by simple methods.Then by substituting them into the atmospheric scattering equation,a more effective solution was obtained this time.By repeating this process,a satisfying solution will be obtained.The second one is the improved algorithm with multi-images(usually two).The two images were taken in the same place under different weather condition.The contours were discrete and the problem becomes a classify problem.The problem can be solved by defined distance inner class and other features with FCM.(4) Proposed two new models based on the atmospheric scattering model and total variation model.The first one was constructed by adding the atmospheric scattering model into the total variation minimization models as a restricting condition.The second one was constructed by using the atmospheric scattering model as the comparability condition,combining it with the total variation and contrast enlargement.(5) Proposed several fast enhancement algorithms for foggy traffic images.The first one provides improved histogram equalization.The main idea for this algorithm is that the contrast is decreased in sky area,and the contrast enlargement is proportional to the distance from a point in the objects to camera.The second one is the fast enhancement algorithm with depth of field compensation in objects.While keeping the advantage in dealing with local high frequency data for histogram equalization,the scattering as depth varying was considered.The third one is the fast algorithm based on the simplified SSR method.In SSR,a lot of convoluted computations are needed,and this processing needs a long computing time.In this thesis,we will present a new,simplified,SSR algorithm that will markedly decrease the computing task.更多还原