A Cascaded Convolutional Neural Network for Single Image Dehazing

In this work, we are proposing a Cascaded Convolutional Neural Network (CNN) for Single Image Dehazing. Images captured under outdoor scenes usually suffer from low contrast and limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite numerous image dehazing methods have been proposed, effective hazy image restoration remains a challenging problem. We proposed CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven sub networks. 

Specifically, the medium transmission estimation sub network is inspired by the densely connected CNN while the global atmospheric light estimation sub network is a lightweight CNN. Besides, these two sub networks are cascaded by sharing the common features. Finally, with the estimated model parameters, the haze-free image is obtained by the atmospheric scattering model inversion, which achieves more accurate and effective restoration performance. Qualitatively and quantitatively experimental results on the synthetic and real-world hazy images demonstrate that the proposed method effectively removes haze from such images, and outperforms several state-of-the-art dehazing methods.