Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.

从数字图像中去除烟雾是各种成像系统中具有挑战性的预处理工具。因此，迄今为止提出了许多烟雾去除（即去烟雾）模型来去除图像中烟雾的影响。去雾模型基于物理模型，这意味着它需要从单个烟雾图像中有效地估计透射图和大气面纱。因此，文献中提出了许多基于先验的恢复模型来估计透射图和大气面纱。然而，这些模型利用了能量函数的计算广泛的最小化。此外，现有的恢复模型存在纹理、边缘和颜色失真等各种问题。因此，在本文中，使用卷积神经网络（CNN）来估计烟雾图像的物理属性。倾斜梯度通道先验（OGCP）用于恢复烟雾图像。最初，获得了烟雾和晴天图像的数据集。此后，我们训练 CNN 从烟雾图像中估计烟雾梯度。最后，基于计算出的烟雾梯度，利用OGCP恢复静止的烟雾图像。性能分析表明，所提出的基于 CNN-OGCP 的除雾模型在各种性能指标方面优于现有的除雾模型。