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Single image haze removal based on the improved atmospheric scattering model

机译:基于改进的大气散射模型的单图像雾度去除

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摘要

In this paper, we propose an improved atmospheric scattering model (IASM) to overcome the inherent limitation of the traditional atmospheric scattering model. Based on the IASM, a fast single image de hazing algorithm is also presented. In this algorithm, by constructing a linear model between the transmission and the haze aware density feature, the transmission map can be directly estimated through a linear operation on three components: luminance, saturation and gradient. Combining the sky-relevant feature and the proposed guided energy model (GEM), we can accurately estimate the atmospheric light and scene incident light, and can further restore the scene albedo via the IASM. Finally, an accelerating framework (AF) based on the Gaussian-Laplacian pyramid is proposed to increase the computational speed. Experimental results demonstrate that the proposed algorithm outperforms most of the prevalent algorithms in terms of visual effect and computational efficiency. Besides, it is also capable of processing various types of degraded images in addition to hazy images. (C) 2017 Elsevier B.V. All rights reserved.
机译:在本文中,我们提出了一种改进的大气散射模型(IASM),以克服传统大气散射模型的固有局限性。基于IASM,提出了一种快速的单图像去雾算法。在该算法中,通过在透射率和雾度感知密度特征之间构建线性模型,可以通过对亮度,饱和度和梯度这三个分量进行线性运算来直接估计透射率图。结合天空相关特征和拟议的制导能量模型(GEM),我们可以准确估算大气光和场景入射光,并可以通过IASM进一步恢复场景反照率。最后,提出了一种基于高斯-拉普拉斯金字塔的加速框架(AF),以提高计算速度。实验结果表明,该算法在视觉效果和计算效率上均优于大多数流行算法。此外,除了模糊图像之外,它还能够处理各种类型的降级图像。 (C)2017 Elsevier B.V.保留所有权利。

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