夜间有雾图像光照不均匀,整体亮度较低,色偏严重,且人工光源周围存在光晕.现有的去雾模型和算法大多针对白天图像,其并不适用于夜间场景,夜间图像去雾颇具挑战性.该文深入分析夜间有雾图像的成像规律,建立含有人工光源的夜间雾天图像成像新模型,并在此基础上提出夜间图像去雾新算法.针对夜间图像光照不均问题,提出基于低通滤波的环境光估计方法,利用估计出的环境光可准确预测夜间场景传输率;针对目前夜间图像去雾后存在光源光晕问题,提出根据图像色度估计场景点属于近光源区域的程度,使算法能自适应地处理光源区域和非光源区域;针对非一致色偏问题,利用直方图匹配方法进行颜色校正.对大量图像进行实验,并与现有白天、夜晚图像去雾算法进行比较,验证了该文提出的夜间雾天图像成像模型及去雾算法的有效性.%The non-uniform illumination, low brightness, serious color deviation and halo effects around artificial light sources lead to the difficulty in haze removal for night-time image. The existing dehazing methods are mostly designed for daytime image and not applicable to nighttime image. This paper focuses on researching nighttime image dehazing. A new nighttime haze model that accounts for the artificial varying light sources is introduced. Based on this new model, a new dehazing framework is proposed. Firstly, the atmospheric light is estimated based on the low pass filter method. This atmospheric light map can be used to predict the transmission of night scene accurately. Secondly, to solve the problem of halo effects around artificial light sources in existing dehazing methods, a method that estimates the distance between the object of the scene and the artificial light sources based on the image chromaticity is proposed. In this way, the scene objects near to the light source region and objects far away from the light source region can be processed respectively. Finally, as for the color cast, an efficient color correction algorithm based on the histogram matching is presented in this paper. Comparing with existing daytime and nighttime dehazing methods, the experimental results of a number of examples demonstrate the effectiveness of the proposed night-time haze model and the dehazing method.
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