Frequency and Spatial Dual Guidance for Image Dehazing

In this paper, we propose a novel image dehazing framework with frequency and spatial dual guidance. In contrast to most existing deep learning-based image dehazing methods that primarily exploit the spatial information and neglect the distinguished frequency information, we introduce a new perspective to address image dehazing by jointly exploring the information in the frequency and spatial domains. To implement frequency and spatial dual guidance, we delicately develop two core designs: amplitude guided phase module in the frequency domain and global guided local module in the spatial domain. Specifically, the former processes the global frequency information via deep Fourier transform and reconstructs the phase spectrum under the guidance of the amplitude spectrum, while the latter integrates the above global frequency information to facilitate the local feature learning in the spatial domain. Extensive experiments on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art approaches both visually and quantitatively. Our code is released publicly at https://github.com/yuhuUSTC/FSDGN .