PDR-Net: Perception-Inspired Single Image Dehazing Network With Refinement

Abstract

During recent years, we have witnessed a rapid development of wireless network technologies which have revolutionized the way people take and share multimedia content. However, images captured in the outdoor scenes usually suffer from limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite the recent progress of image dehazing methods, the visual quality of dehazed results still needs further improvement. In this paper, we propose a deep convolutional neural network (CNN) for single image dehazing called PDR-Net, which includes a perception-inspired haze removal subnetwork that reconstructs the latent dehazed image and a refinement subnetwork that further enhances the contrast and color properties of the dehazed result by joint multi-term loss optimization. Compared to the previous methods, our method combines the advantages of existing indoor and outdoor image dehazing training data, which makes the proposed PDR-Net generalized to various hazy images and effective for improving the visual quality of the dehazed results. Extensive experiments demonstrate that the proposed method achieves comparable and even better performance on both real and synthetic images in qualitative and quantitative metrics. Additionally, the potential usage of our method in high-level vision tasks is discussed.