A Cascaded Convolutional Neural Network 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 photographs. Despite numerous image dehazing methods have been proposed, effective hazy image restoration remains a challenging problem. Existing learning-based methods usually predict the medium transmission by convolutional neural networks (CNNs), but ignore the key global atmospheric light. Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven subnetworks. Specifically, the medium transmission estimation subnetwork is inspired by the densely connected CNN while the global atmospheric light estimation subnetwork is a light-weight CNN. Besides, these two subnetworks are cascaded by sharing the common features. Finally, with the estimated model parameters, the hazefree 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.This work was supported in part by the National Key Basic Research Program of China under Grant 2014CB340403, in part by the Natural Science Foundation of Tianjin of China under Grant 15JCYBJC15500, in part by the National Natural Science Foundation of China under Grant 61771334, in part by the Tianjin Research Program of Application Foundation and Advanced Technology under Grant 15JCQNJC01800, in part by the Program of China Scholarships Council (CSC) under Grant CSC 201606250063, and in part by the National Natural Science Foundation of China under Grant 61632081