In natural scenarios, vision models often encounter the challenge of complex degradation scenarios(e.g., rain, snow, fog, or motion blur). These degradations severely corrupt image features, causing existing models to treat rarely seen or unseen degraded images as “unfamiliar”, thereby losing their inherent recognition and perception capabilities. To address this challenge, we propose a novel degradation disentanglement model (DDM) aimed at precisely disentangling degraded features from the image. The model enhances its perception of various degradations by controlling the matching of features across different degradation types and further strengthens the cross-correlation of target features by introducing a degradation suppression module. This enables the model to re-identify and re-localize targets while removing degradations. We validated the effectiveness of our method on more challenging few-shot segmentation datasets Degraded-Pascal and Degraded-COCO. Results on them outperform SOTA with 3.71% and 3.69% improvement respectively. The experimental results show that our method significantly improves the performance of vision models in various degradation scenarios and provides new ideas and solutions for visual understanding tasks in complex environments.