The current CNN-based methods exhibit satisfactory performance in fog removal， but their network robustness is compromised due to the intricate haze distribution and challenging dataset collection. Consequently， there is a significant loss of texture details during the fog removal process and severe overfitting issues on small-scale data sets. To address these challenges， we propose a two-branch structure incorporating space-frequency joint techniques. The upper branch focuses on capturing finer texture details by utilizing three-level wavelet transform to extract features in the frequency domain. Meanwhile， the lower branch enhances network generalization by employing domain migration method to incorporate additional prior information from airspace and leveraging Res2Net as its core component. Finally， the proposed model is trained on the NH-HAZE dataset and evaluated for generalization ability using the I-HAZE and NTIRE 2023 datasets. Furthermore， to ensure fairness in comparison experiments， all competing algorithms are also trained using the NH-HAZE dataset. Experimental results demonstrate that our proposed network significantly improves both detail texture recovery capability and generalization performance.