Deep fully convolutional regression networks for single image haze removal

Haze removal for a single image is known to be a challenging ill-posed problem in computer vision. The performance of existing prior-based image dehazing methods is limited by the effectiveness of hand-designed features. The emerging con-volutional neural network (CNN) based approaches can remove haze with the automatically learned intrinsic mapping between the input hazy images and their corresponding transmission maps, but the recovered haze-free images sometimes are still unsatisfactory. In order to improve the dehazed images, we aim to develop an effective deep fully convolutional regression network for more accurate transmission estimation. Our network is an end-to-end regression system which take input of arbitrary size hazy image and predict correspondingly-sized transmission map. To train and evaluate deep network for image dehazing efficiently, we develop new outdoor synthetic training set respectively. In addition, we fully compare the existing CNN-based haze removal approaches with our algorithm on real-world images and our synthesized benchmark dataset. The experimental results demonstrate that our trained regression model achieves superior dehazing performance than the current state-of-the-art methods.