Abstract
Endoscopic surgery relies on intraoperative video, making image quality a decisive factor for surgical safety and efficacy. Yet, endoscopic videos are often degraded by uneven illumination, tissue scattering, occlusions, and motion blur, which obscure critical anatomical details and complicate surgical manipulation. Although deep learning-based methods have shown promise in image enhancement, most existing approaches remain too computationally demanding for real-time surgical use. To address this challenge, we propose a degradation-aware framework for endoscopic video enhancement, which enables real-time, high-quality enhancement by propagating degradation representations across frames. In our framework, degradation representations are first extracted from images using contrastive learning. We then introduce a fusion mechanism that modulates image features with these representations to guide a single-frame enhancement model, which is trained with a cycle-consistency constraint between degraded and restored images to improve robustness and generalization. Experiments demonstrate that our framework achieves a superior balance between performance and efficiency compared with several state-of-the-art methods. These results highlight the effectiveness of degradation-aware modeling for real-time endoscopic video enhancement. Nevertheless, our method suggests that implicitly learning and propagating degradation representation offer a practical pathway for clinical application.
Abstract (translated)
腹腔镜手术依赖于术中视频，因此图像质量是决定手术安全性和有效性的关键因素。然而，内窥镜视频常常受到不均匀照明、组织散射、遮挡和运动模糊等因素的影响，这些都会掩盖重要解剖细节并使手术操作复杂化。尽管基于深度学习的方法在图像增强方面显示出巨大潜力，但大多数现有方法仍然过于计算密集型而无法实现实时手术应用。为了解决这一挑战，我们提出了一种针对内窥镜视频增强的降级感知框架，该框架能够通过跨帧传播降级表示实现实时、高质量的增强。 在我们的框架中，首先使用对比学习从图像中提取降级表示。接着引入一种融合机制，利用这些表示调节图像特征，并指导单帧增强模型进行操作。我们通过在受损和恢复图像之间施加循环一致性约束来训练这个模型，以提高其鲁棒性和泛化能力。 实验表明，我们的框架与几种最先进的方法相比，在性能和效率上取得了更优的平衡。这一结果突显了降级感知建模在实时内窥镜视频增强中的有效性。然而，我们提出的方法暗示通过隐式学习并传播降级表示可以为临床应用提供实用途径。
URL
https://arxiv.org/abs/2512.07253
