The quality of the images captured by the camera deteriorates in hazy weather, which affects the effectiveness of subsequent high-level vision applications. Extensive methods have been proposed to remove haze and restore clear and haze-free images. However, these methods mainly focus on the accuracy of the model, while ignoring the computational complexity and inference speed that make it difficult to deploy on resource-constrained devices. To address the aforementioned problems, we propose a simple but effective single-image dehazing network. The network is based on the classical U-Net architecture. First, the encoder uses a down-sampling operation to extract shallow hierarchical features and reduce the dimension of the features. Then, the degraded features are gradually restored through the cascaded feature recovery module. Finally, the deep and shallow features are fused through the decoder to obtain the recovered images. The proposed method leverages the stacked larger kernel convolutions to enhance the local and global feature learning capability, and better cope with the severe degradation of image quality in dense and non-homogeneous haze weather through the hybrid attention mechanism.