Frequency-aware Deep Dual-path Feature Enhancement Network for Image Dehazing

Single image dehazing is a challenging task due to the severe degradations caused by the particles in the air. Recently, various CNN-based methods have been proposed and they have achieved promising results on some dehazing tasks. However, the existing end-to-end dehazing networks process high-frequency information and low-frequency information at the same time. Therefore, most dehazing methods cannot restore dehazed image with satisfying high-frequency details. In this paper, we propose a Frequency-aware deep Dual-path Feature enhancement Network (FDF-Net) to better restore the high-frequency information while removing the haze. To achieve this, we introduce a Dual-path Feature Enhancement (DFE) block, which contains two branches: one path is to remedy the missing spatial information from high-resolution features, and the other one is to obtain new features to increase the variety of features. We believe the dual-path architecture can help the first path to focus on the recovering the high-frequency information. Furthermore, to reserve more detailed image information from the features with larger resolution, we adopt a wavelet transform module during the downsampling process of the encoder module to directly pass the high frequency information to the next level. The extensive experiments show the superiority of the proposed model over previous methods on the benchmark datasets as well as real-world hazy images.