Multi-scale-based Network for Image Dehazing

Image and video dehazing is a difficult subject that has received a lot of attention in the field of computer vision. The presence of air haze in photos and movies can reduce visual quality dramatically, resulting in a loss of contrast, color accuracy, and sharpness. To address this issue, in this paper, we propose a deep-learning-based method for image dehazing. The proposed network consists of using multi-scale representation at every VGG-16 block to conserve the high quality of the image during the learning process. The collaboration of convolutional layers and the multi-scale block make the network learn from different scales combined with the outputs of the previous layers of the networks. This can conserve the high quality as well as remove the haze. The proposed method is trained and tested on four datasets including BESIDE, DENSE, O-HAze, and I-HAZE, and hives promising results compared to some of the state-of-the-art methods.