恶劣天气下图像的清晰化方法研究

发布时间：2018-03-30 07:49

本文选题：图像去雾　切入点：视频去雾　出处：《长春工业大学》2016年硕士论文

【摘要】：户外视觉系统由于暴露在开放的外界环境中,会常常受到恶劣天气会影响而无法正常的工作,使得捕获的图像和视频出现严重降质的现象。其中,近年来出现最频繁的恶劣天气是雾霾天气,已经成为严重影响成像质量的重要因素。在雾霾天气下,由于大气散射作用,会导致捕获的图像模糊不清,信息的可辨识度降低。雾天图像的应用价值大大减小,会干扰其它和图像处理相关的领域的正常工作。此外,我国近年来雾霾天气频发,图像去雾技术成为越来越重要的研究课题,具有十分重要的实际意义。由于图像去雾问题涉及到天气条件的随机性与复杂性,这增大了其研究难度。虽然国内外已有的图像去雾算法很多,但均有一定的局限性和相应的缺点,这些算法在本质上都可以大致归类为两种:第一种为基于非物理模型的图像增强方法,第二种为基于物理模型的图像复原方法。首先,提出了一种基于物理模型的图像去雾算法,利用改进的White Patch Retinex算法计算出全球大气光的值,提出了一种基于去雾后图像的信息损失来估算透射率的方法,通过最小化信息损失方法估计出初始透射率,然后使用导向滤波对初始透射率图进行快速细化,进而得到较精确的最终的透射率图,最后通过反解雾天图像退化物理模型得到清晰无雾的结果图像。实验结果表明,利用该方法得到的结果图像较明亮,不需要再进行额外的亮度增强操作,且在天空区域不会出现严重的颜色失真现象,鲁棒性和适用性较高其次,对视频去雾进行研究。通过将有雾视频帧的RGB颜色空间转换到YUV颜色空间,仅对其中的亮度分量Y进行去雾处理,保持色度分量(U,V)不变,降低了算法的复杂度,大大提高了处理过程的运行速度,从而能适用于处理视频序列,处理速度可达20-22帧每秒,具有良好的实时性。最后,研究了户外监控视频的去雾方法,因为监控视频的背景具有静止不变的特性,所以本文将视频帧的背景图像和前景运动目标图像分别提取出来后,各自进行去雾处理,然后融合为完整的无雾监控视频。由于对背景图像是静止且不变的,所以只对背景图像做一次去雾处理,大大缩小了视频去雾算法的运算时间,具有很好的实用意义。
[Abstract]:Because the outdoor visual system is exposed to open environment, it is often affected by bad weather and can not work properly, resulting in serious degradation of captured images and videos. The most frequent bad weather in recent years is haze weather, which has become an important factor that seriously affects the imaging quality. In haze weather, because of atmospheric scattering, the captured images are blurred. The application value of fog images is greatly reduced, which will interfere with the normal work of other fields related to image processing. In addition, in recent years, haze weather occurs frequently in China. Image de-fogging technology has become a more and more important research topic, which has very important practical significance. Because the problem of image de-fogging involves the randomness and complexity of weather conditions, Although there are a lot of image de-fogging algorithms at home and abroad, they all have some limitations and corresponding shortcomings. In essence, these algorithms can be roughly classified into two categories: the first is an image enhancement method based on a non-physical model, and the second is an image restoration method based on a physical model. An image de-fogging algorithm based on physical model is proposed in this paper. The improved White Patch Retinex algorithm is used to calculate the global atmospheric light, and a method to estimate the transmittance based on the information loss of the de-fogged image is proposed. The initial transmittance is estimated by minimizing the loss of information, and then the initial transmittance map is refined quickly by the guidance filter, and a more accurate final transmittance map is obtained. Finally, a clear and fog-free result image is obtained by using the degenerated physical model of the fog image. The experimental results show that the result image obtained by this method is brighter, and no additional brightness enhancement operation is needed. And there is no serious color distortion in the sky region. Secondly, the RGB color space of the fogged video frame is converted to the YUV color space. Only the luminance component Y is de-fogged to keep the chrominance component UV constant, which reduces the complexity of the algorithm and greatly improves the speed of the processing process, which can be applied to the processing of video sequences. The processing speed can reach 20-22 frames per second. Finally, the de-fogging method of outdoor surveillance video is studied. Because the background of the surveillance video is static and invariant, the background image of the video frame and the foreground moving target image are extracted respectively in this paper. Because the background image is still and invariable, only one de-fogging process is made for the background image, which greatly reduces the computation time of the video de-fog algorithm. It has good practical significance.
【学位授予单位】：长春工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41

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