EndoTME: Physics-Inspired Degradation-Aware Trajectory Memory for Endoscopic Video Enhancement

Endoscopic video enhancement is crucial for improving surgical visibility and supporting reliable intraoperative visual perception. However, existing video restoration methods commonly rely on generic temporal feature propagation, which suffers from two key limitations. First, they tend to indiscriminately aggregate historical frame information that may already be distorted or degraded. Second, they lack explicit awareness of endoscopy-specific degradations, making them less effective in challenging surgical scenes involving blood haze, specular reflection, motion blur, low contrast, and temporal instability. To address these issues, we propose EndoTME, an endoscopic video enhancement framework that improves the reliability of temporal information and the adaptivity to complex degradations. Specifically, we develop a reliable trajectory memory retrieval mechanism to selectively exploit trustworthy historical features while suppressing unreliable temporal cues caused by severe image degradation or unstable motion. Such reliability-aware memory selection reduces the cross-frame propagation of corrupted temporal information and improves the temporal coherence of enhanced videos. Meanwhile, we introduce a degradation conditioning mechanism based on physics-inspired visual cues, which explicitly characterizes typical endoscopic degradations and facilitates adaptation of the restoration process to different local degradation patterns. Experiments on a paired synthetic dataset and two real-world endoscopic video datasets demonstrate that EndoTME outperforms representative image and video restoration baselines.