结合光源分割和线性图像深度估计的夜间图像去雾

Nighttime image dehazing with a new light segmentation method and a linear image depth estimation model

夜间有雾图像通常具有对比度低、光照不均匀、颜色偏移以及噪声较多等现象,这些退化现象使得夜间图像去雾具有极大的挑战性。针对夜间图像存在的退化问题,本文提出了一种能够在夜间图像中有效去雾并提高图像质量的方法。首先,将图像分解成光晕层和有雾层,并对有雾层进行颜色校正。其次,通过一种新提出的带有伽马变换的图像光源分割方法来分割光源,并设置分割阈值作为像素点属于光源区域的概率值。然后,将得到的概率值与最大反射先验相结合来估计光源和非光源区域的大气光值。最后,根据图像深度与亮度、饱和度以及梯度之间的关系建立线性模型,进一步估计透射率的值。实验得到的分割阈值为0.07,线性深度估计参数分别为1.0267、−0.5966、0.6735、0.004135。实验结果表明本文方法在夜间图像去雾、消除光晕、减少噪声,以及提高可视度方面取得良好的效果。

Image with the scene of haze at night has low contrast,non-uniform illumination,color cast and significant noise.These cause nighttime haze removal from single image to be problematic and challenging.In this paper,we put forward a method that can remove nighttime haze from images and improve image quality.The input image is first decomposed into a glow layer and a haze layer with a modified color channel transformation for glow artifacts and color correction.A new light segmentation function is proposed next by using gamma correction of the channel difference and setting the threshold levels as the probability of a pixel belonging to a light source region.Then we estimate the ambient illuminance map by combining the maxim um reflectance prior value with the aforementioned probability and computing the atmospheric light in the light and non-light regions.Finally,we establish a novel linear model to build the connection between the image depth map and three image features including luminance,saturation and gradient map for the light source regions while using the dark channel prior for the non-light source regions.The result of the light segmentation is 0.07,and the parameters of the linear depth estimation are 1.0267,−0.5966,0.6735 and 0.004135.Experimental results show the proposed method is reliable for removing nighttime haze and glow of active light sources,reducing significant noise and improving visibility.