DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement
This addresses image quality issues in computer vision applications like dehazing, but it is incremental as it builds on existing deep learning approaches.
The paper tackled the problem of single image dehazing by proposing DR-Net, a deep network that uses transmission prediction and weakly supervised refinement, achieving superior performance over state-of-the-art methods on synthetic and real images in qualitative and quantitative metrics.
Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications. To tackle these problems, we propose a new deep network architecture for single image dehazing called DR-Net. Our model consists of three main subnetworks: a transmission prediction network that predicts transmission map for the input image, a haze removal network that reconstructs latent image steered by the transmission map, and a refinement network that enhances the details and color properties of the dehazed result via weakly supervised learning. Compared to previous methods, our method advances in three aspects: (i) pure data-driven model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and applicability. Extensive experiments demonstrate that our DR-Net outperforms the state-of-the-art methods on both synthetic and real images in qualitative and quantitative metrics. Additionally, the utility of DR-Net has been illustrated by its potential usage in several important computer vision tasks.