DCNet: dual-cascade network for single image dehazing

Image dehazing is a challenging ill-posed problem in the field of computer vision. Existing learning-methods usually use a single convolutional neural network (CNN) model to solve it, which lacks details recovery mechanism and leads to poor performance. In this paper, we propose an end-to-end Dual-cascade Network for image dehazing, which obtains the haze-free image in a coarse-to-fine manner. Specifically, the overall model consists of two sub-networks: Net-U and Net-D. The Net-U employs an encoder–decoder architecture to restore a coarse dehazing result, which leverages residual channel attention block for distilling hierarchical features, and transmits the contextual information into next stage. To preserve the spatial details of latent image, our Net-D adopts a constant-size CNN structure, and captures the texture-rich features by utilizing residual multi-scale spatial block. Moreover, we apply an effective selective fusion module to integrate these derived features from Net-U and Net-D. Experimental comparisons show that our method obtains comparable or even better results than existing state-of-the-art methods in terms of quantitative evaluation and visual performance. The code will be made publicly available on GitHub.