Multi-Scale Feature Fusion Dehazing Network Based on U-net

To address the problem of reduced clarity caused by detail information degradation during haze processing in complex scenes, this study presents a multi-scale feature fusion dehazing network based on U-net. In the encoder component, we employ a dynamic large kernel convolution with a dynamic weighting mechanism to enhance global information extraction. This mechanism allows for adaptive adjustment of feature weights, thereby improving the model's adaptability to complex scenes. In addition, we introduce a parallel feature attention module PA1 to capture critical details and color information in images, effectively mitigating the loss of important features during the dehazing process. To tackle the challenges posed by complex illumination changes and uneven haze conditions, we incorporate coordinate attention in the decoder's parallel feature attention module PA2. This approach integrates spatial and channel information, allowing for a more comprehensive capture of key details in feature maps. Experimental results show that the proposed network model achieves excellent dehazing effects across various datasets. The proposed network model outperforms classical dehazing networks such as FFA-Net and AOD-Net, effectively addressing detail loss while providing superior image dehazing performance.