Residual-based Fast Single Image Fog Removal
Yeming Lin, Yunjian Zhang, Tong Li, Jingguo Ge · 2019
Single image haze removal is a challenging problem to address, and various constraints/priors have been previously considered to obtain acceptable dehazing solutions. In this paper, we propose a trainable end-to-end system for single image dehazing called ReDehazeNet based on the residual and dilation convolutional neural networks. The first part of the networks incorporated into the system is used for recovering a coarse clear image, which is predicted by adopting a context aggregation sub-network that can capture the global structure information. The second part of the network adopts a novel hierarchical convolutional neural network to further refine the details of the clean image by integrating the local context information. Experiments on benchmark images show that ReDehazeNet outperforms several existing state-of-the-art methods while being highly efficient and easy to use.