Focusing on both degradation type and severity simultaneously may introduce complexity to the model architecture and training process. Handling multiple factors at once could increase computational requirements and training time. Moreover, incorporating both type and severity information might require a larger dataset with diverse samples to effectively capture all variations in weather degradations. Balancing between these two aspects while maintaining performance levels could also pose a challenge in terms of optimization.