Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.

我们提出了一种快速的深度多块分层网络，通过聚合来自迷雾图像不同空间段的多个图像块的特征，从而以更少的网络参数恢复非均匀雾图像。与当前的多尺度方法相比，我们的方法提供更快的运行时，并展示了在密集雾去除方面相对于其他最先进模型的优势。

快速深度多补丁分层网络用于非均质图像去雾