Kumar Ashok, Jain Arpit
College of Computing Science and Information Technogy, Teerthanker Mahaveer University, Moradabad, 244001 India.
Front Comput Sci (Berl). 2021;15(6):156706. doi: 10.1007/s11704-020-9305-8. Epub 2021 Jun 28.
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.
Supplementary material is available in the online version of this article at 10.1007/s11704-020-9305-8.
去除数字图像中的雾霾是各种成像系统中一项具有挑战性的预处理工具。因此，到目前为止已经提出了许多雾霾去除（即去雾）模型来消除图像中的雾霾影响。去雾模型基于物理模型，这意味着它需要从单个有雾图像中有效地估计传输图和大气遮罩。因此，文献中提出了许多基于先验的恢复模型来估计传输图和大气遮罩。然而，这些模型利用了计算量很大的能量函数最小化。此外，现有的恢复模型存在各种问题，如图像纹理、边缘和颜色的失真。因此，在本文中，使用卷积神经网络（CNN）来估计有雾图像的物理属性。利用倾斜梯度通道先验（OGCP）来恢复有雾图像。首先，获取有雾图像和晴朗图像的数据集。此后，我们训练CNN从有雾图像中估计雾霾梯度。最后，基于计算出的雾霾梯度，利用OGCP来恢复仍然有雾的图像。性能分析表明，所提出的基于CNN-OGCP的去雾模型在各种性能指标方面优于现有的去雾模型。
补充材料可在本文的在线版本中获取，链接为10.1007/s11704-020-9305-8。
