摘要
为提高户外视觉系统在有雾天气条件下工作的可靠性和鲁棒性,本文针对视觉系统雾天采集图像的退化问题,在分析其退化机理的基础上,从图像增强和图像复原两个角度对雾天降质图像的清晰化技术进行了比较深入的研究,在对已有雾天降质图像清晰化技术进行完善、改进和引入新思路等方面做了一些有意义的工作。
对比度降质与深度密切相关是雾天降质图像的一个重要特征,如何在考虑场景深度变化的基础上更好地提高图像的对比度是算法需要解决的问题。基于移动模板的雾天图像景物影像清晰化方法从局部对比度增强的角度提出了一个实现雾天降质场景清晰化处理的新思路,本文针对该算法中存在的一些不足之处,从天空区域赋值、移动模板的步长等方面对算法进行改进,给出了相应的处理对策。实验结果表明改进算法在改善视觉效果的同时,一定程度上克服了原算法计算时间过长的不足。
基于移动模板的雾天景物影像清晰化方法及改进算法都是通过假设模板内各场景点深度处处相等,实现对局部深度信息的匹配,因此,无法对模板内存在深度突变的情况进行有效地反馈。为了解决这个问题,本文进一步提出了一种基于深度区域分割预处理的雾天降质图像清晰化算法。该算法首先将降质图像场景划分为若干个不同的深度区域,然后在各同深度区域内,利用全局直方图均衡化函数实现对比度的增强处理。该算法由于考虑了雾天降质图像对比度降质与深度密切相关的特点,可以对雾天降质图像实现有效的增强,且由于其避开了对场景具体深度信息的求取,因而降低了问题的难度。
考虑到有雾天气下大气粒子的散射作用导致图像对比度降低,纹理细节丢失,图像比较模糊的特点,本文结合模糊理论处理不确定问题的优势,提出了一种基于模糊对比度增强的雾天降质图像清晰化算法。该算法在对模糊对比度的修正过程中,合理地将对比度降质随深度呈指数增加的规律融入其中,实现了雾天降质图像对比度的自适应增强处理。
基于退化模型实现场景的清晰化复原问题归根结底在于模型参数的求取,即景深参数和大气散射系数,为了避开昂贵的测距硬件设备以及利用单幅图像进行深度信息估计不可靠的缺点,本文从降质图像的特点出发,结合大气散射作用对图像对比度的影响随深度指数性增加的规律,提出了一种利用多幅降质图像复原光学深度的算法,该算法利用两幅不同天气下的降质图像作为输入,同时用到图像的灰度信息和对比度信息实现图像各点光学深度的估计计算。对场景的清晰化复原实验证明了光学深度估计算法的有效性。
为了解决对单幅雾天降质图像的清晰化问题,本文对雾天降质图像的退化模型进行了改进,并在改进模型的基础上,进一步提出了一种新颖的雾天降质图像复原算法。该算法利用遗传算法良好的全局寻优能力实现对模型权重向量最优参数的搜索,将雾天降质图像的清晰复原问题转化为在全局对比度最优意义下对原始未退化图像的最优估计问题。该算法避开了复杂的深度、大气散射系数等参数的求解,实现简单,对单幅雾天降质图像的清晰化实验取得了满意的视觉效果。
Under fog weather conditions, the contrast and color characters of the images captured by outdoor surveillance system are drastically degraded. In order to improve system's reliability and stability for object features detection, this paper focus the research on the fog-degraded image clearness techniques based on the analysis of image degradation mechanisms, and some novel/improved ideas and methods have been proposed mainly from two aspects which include image enhancement and image restoration.
One of the main characteristics for fog-degraded images is that the local image contrast depends strongly on distance, that is to say, the level of contrast reduction increase exponentially with the distance from the camera to the object. As a result, conventional space invariant filtering techniques fail to adequately remove weather effects from images. Fog-degraded image clearness method based on moving mask proposed a novel idea for scene contrast enhancement, which does not need any scene depth information and avoid complicated atmospheric scattering model. However, computation complexity of the method is high and there is overlong time processing problem, aiming to solve this problem, an improved fog-degraded image enhancement algorithm is proposed in this paper and experimental results on fog-degraded images demonstrate that the proposed improved algorithm can gain better visual effect and solve the problem of long time processing to a certain extent.
Considering that above improved method implements contrast enhancement by assuming all the pixels in moving mask with same depth, therefore, it can't deweather the fog-degraded images effectively when the scene in the mask has abrupt depth. In order to solve this problem, a novel fog-degraded image clearness algorithm based on depth region segmentation reprocessing is presented in this paper. The algorithm divides image scene into some different depth regions firstly, then enhancing the contrast in each depth region by histogram equalization function. By applying depth region segmentation reprocessing, this algorithm skillfully combines the important characteristic of fog-degraded images that the local image contrast depends strongly on distance and avoids exact depth information, therefore, efficiently decreases computing time and improve the visual effect.
The images captured in fog weather usually have lower contrast and fuzzy object boundary, by combining the merit of fuzzy theory for uncertain information, this paper proposes a novel fog-degraded image clearness algorithm based on fuzzy contrast enhancement, which reasonably apply contrast exponential degrade law in fuzzy contrast measure modification, can perform fog-degraded image enhancement adaptively.
For the problem of clearness restoration of fog-degraded images, it is of utmost importance to make a correct estimation of the parameters in the atmospheric scattering model, that is, the scattering coefficient and the depths of scene points. For the purpose of avoiding expensive hardware and the uncertainty from one fog-degraded image for distance, this paper proposed a new optics depth estimation method from two degraded images in different weather by reasonably combining the contrast exponential degrade law. Experimental results demonstrate satisfying scene restoration performance.
In order to solve the clearness problem from single fog-degraded image, this paper improved the fog image degraded model and proposed a novel fog-degraded image restoration algorithm based on the improved model. By integrating the merit of genetic algorithm for searching global optimal parameters in this algorithm, the problem of fog-degraded images clearness restoration is transformed into the problem of optimization estimation for original undegraded image by maximizing the global contrast object function, in this way, the proposed algorithm can restore the object image as completely as possible in probability sense. Experimental results for single object image restoration gain satisfying visual effect.
引文
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