Real-Time Endoscopic Video Enhancement via Degradation Representation Estimation and Propagation

TL;DR

This paper introduces a real-time method to enhance endoscopic videos by estimating and propagating image degradation representations, improving surgical visualization.

Contribution

The novel framework uses degradation representation estimation and propagation to achieve real-time endoscopic video enhancement with high quality.

Findings

The proposed framework achieves a balance between enhancement quality and computational efficiency.

Downstream segmentation tasks show improved surgical scene understanding with the proposed method.

Abstract

Endoscopic images are often degraded by uneven illumination, motion blur, and tissue occlusion, which obscure critical anatomical details and complicate surgical manipulation. This issue is particularly pronounced in single-port endoscopic surgery, where the imaging capability of the camera is further constrained by limited working space. While deep learning-based enhancement methods have demonstrated impressive performance, most existing approaches remain too computationally demanding for real-time surgical use. To address this challenge, we propose an efficient stepwise endoscopic image enhancement framework that introduces an implicit degradation representation as an intermediate feature to guide the enhancement module toward high-quality results. The framework further exploits the temporal continuity of endoscopic videos, based on the assumption that image degradation evolves smoothly over short time intervals. Accordingly, high-quality degradation representations are estimated only on key frames at fixed intervals, while the representations for the remaining frames are obtained through fast inter-frame propagation, thereby significantly improving computational efficiency while maintaining enhancement quality. Experimental results demonstrate that our method achieves an excellent balance between enhancement quality and computational efficiency. Further evaluation on the downstream segmentation task suggests that our method substantially enhances the understanding of the surgical scene, validating that implicitly learning and degradation representation propagation offer a practical pathway for real-time clinical application.

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