Attention-adaptive multi-scale feature aggregation dehazing network

In this paper, we propose an end-to-end Attention-adaptive Multi-scale Feature Aggregation Dehazing Network (AMA-Net). The AMA-Net is based on U-Net and designs with three attention-driven modules, Joint Attention Residual Block (JAB), Joint Attention Feature Aggregation Group (JAAG), and Layer Adaptive Attention Feature Aggregation Module (LAA). To be more specific, considering the unevenly distributed haze in images, we introduce the JAB, which adaptively assigns weights to make networks pay attention to important features; to fully utilize the residual features, we propose the residual aggregation (via three JABs) in JAAG; since most feature aggregation methods for dehazing networks do not filter and refine features at different layers, we add LAA to the decoder to weight the features at different layers for aggregation. Through the ablation studies, we verify the effectiveness of the JAB, JAAG, and LAA. Experimental results on synthetic and real-world datasets show that the proposed AMA-Net outperforms relevant state-of-the-art methods.