In view of the fact that the existing defog methods often ignore the key atmospheric light estimation, a method based on quadtree decomposition is proposed, which avoids the influence of bright white area on atmospheric light estimation and accurately estimates atmospheric light in the sky region. In order to avoid the limitation of manual feature extraction, three convolution scales are used to check the original fog image for convolution operation, and the propagation map to be refined is

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... ined after a series of feature learning of the network, and then the image fusion method is used to refine it. Finally, the estimated parameters are brought into the atmospheric scattering model to deduce a clear image. The quantitative and qualitative experimental results of synthetic and real-world grain depot fog and dust images show that the algorithm has a good effect on image texture details and sky region processing, and has high robustness and universality. Ó 2020 Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Z. Li et al., Grain depot image dehazing via quadtree decomposition and convolutional neural networks, Alexandria Eng. J. (2020), https://doi.