SRKTDN: Applying Super Resolution Method to Dehazing Task

Tianyi Chen, Jiahui Fu, Wentao Jiang, Chen Gao, Si Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 487-496

Nonhomogeneous haze removal is a challenging problem, which does not follow the physical scattering model of haze. Numerous existing methods focus on homogeneous haze removal by generating transmission map of the image, which is not suitable for nonhomogeneous dehazing tasks. Some methods use end-to-end model but are also designed for homogeneous haze. Inspired by Knowledge Transfer Dehazing Network and Trident Dehazing Network, we propose a model with super-resolution method and knowledge transfer method. Our model consists of a teacher network, a dehaze network and a super-resolution network. The teacher network provides the dehaze network with reliable prior, the dehaze network focuses primarily on haze removal, and the super-resolution network is used to capture details in the hazy image. Ablation study shows that the super-resolution network has significant benefit to image quality. And comparison shows that our model outperforms previous state-of-the-art methods in terms of perceptual quality on NTIRE2021 NonHomogeneous Dehazing Challenge dataset, and also performs well on other datasets.