Feature Fusion Image Dehazing Network Based on Hybrid Parallel Attention

Abstract

Most of the existing dehazing methods ignore some global and local detail information when processing images and fail to fully combine feature information at different levels, which leads to contrast imbalance and residual haze in the dehazed images. To this end, this article proposes a image dehazing network based on hybrid parallel attention feature fusion, called the HPA-HFF network. This network is an optimization of the basic network, FFA-Net. First, the hybrid parallel attention (HPA) module is introduced, which uses parallel connections to mix different types of attention mechanisms, which can not only enhance the extraction and fusion capabilities of global spatial context information but also enhance the expression capabilities of features and have better dehazing effects on uneven distribution of haze. Second, the hierarchical feature fusion (HFF) module is introduced, which dynamically fuses feature maps from different paths to adaptively increase their receptive field and refine and enhance image features. Experimental results demonstrate that the HPA-HFF network proposed in this article is contrasted with eight mainstream dehazing networks on the public dataset RESIDE. The HPA-HFF network achieves the highest PSNR (39.41) and SSIM (0.9967) and obtains a good dehazing effect in subjective vision.