Abstract Outdoor images taken in inclement weather conditions are often contaminated with colloidal particles and droplet in the atmosphere. These captured images are susceptible to low contrast, poor visibility, and color distortion, which is the reason for serious errors in digital image vision systems. Therefore, defogging research has material significance for practical applications. In this paper, image dehazing is regarded as a mathematical inversion and image restoration process on the basis of fog image degradation model. The global atmospheric light A can be approximately estimated by combining Gaussian low-pass filtering with the single-threshold segmentation and binary tree method. And a deep learning transmittance network is adopted to modify transmittance. Comparison experimental results show that our method is effective in dealing with thick fog, complex scenes and multicolor images. In addition, our method is superior to four other state-of-the-art defogging methods in visual impact, universality and running speed. Graphic abstract The overall framework of our method

中文翻译：

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DLT-Net：用于单幅图像去雾的深度学习透射网络

摘要 在恶劣天气条件下拍摄的户外图像经常被大气中的胶体颗粒和液滴污染。这些捕获的图像容易受到低对比度、低能见度和颜色失真的影响，这是数字图像视觉系统出现严重错误的原因。因此，除雾研究对实际应用具有重要意义。本文将图像去雾视为基于雾图像退化模型的数学反演和图像恢复过程。将高斯低通滤波与单阈值分割和二叉树方法相结合，可以近似估计全球大气光A。并采用深度学习透光网络修改透光率。对比实验结果表明，我们的方法在处理浓雾、复杂场景和多色图像方面是有效的。此外，我们的方法在视觉冲击力、通用性和运行速度方面优于其他四种最先进的除雾方法。图形摘要 我们方法的总体框架