Model driven single image dehazing was widely studied on top of different priors due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine structure in the image. A multi-scale dehazing algorithm is then proposed to address the latter problem by adopting Laplacian and Guassian pyramids to decompose the hazy image into different levels and applying different haze removal and noise reduction approaches to restore the scene radiance at different levels of the pyramid. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperforms state of the art dehazing algorithms and the noise is indeed prevented from being amplified in the sky region.
翻译:由于应用范围很广,在不同的前科上广泛研究了以模型驱动的单一图像脱色。物体弧度和烟雾之间的模糊度以及天空区域的噪声放大度是模型驱动的单一图像脱色的两个固有问题。在本文中,为了解决前一个问题,建议了一种暗直接淡化之前(DAP)的暗直接淡化方法。提出了一种新颖的烟雾线平均值,以减少由DAP引起的形态性人工制品。DAP使一个带有较小半径的加权制导图像过滤器能够进一步减少形态性文物,同时保持图像的精细结构。然后建议一种多尺度的除色算法来解决后一个问题,采用Laplacian和Guassian等金字塔,将烟雾图像分解到不同层次,并采用不同的除色和减少噪音的方法恢复金字塔不同层次的场景色。由此产生的金字塔被坍塌,以恢复无烟雾的图像。实验结果表明,拟议的算法超越了艺术脱色算法的外状态,而且噪音确实无法在天空区域放大。