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

Area of Science:

• Medical Imaging
• Computer Vision
• Surgical Technology

Background:

• Endoscopic images suffer from degradation (illumination, blur, occlusion), hindering surgical navigation.
• Single-port endoscopic surgery exacerbates imaging limitations due to confined spaces.
• Current deep learning enhancement methods are often too computationally intensive for real-time surgical applications.

Purpose of the Study:

• To develop an efficient endoscopic image enhancement framework for real-time surgical use.
• To address the computational demands of existing deep learning enhancement techniques.
• To improve the clarity of anatomical details in endoscopic videos for better surgical understanding.

Main Methods:

• Proposed an efficient stepwise endoscopic image enhancement framework.
• Introduced an implicit degradation representation as an intermediate feature for guided enhancement.
• Exploited temporal continuity in endoscopic videos, estimating degradation representations on key frames and propagating them for efficiency.

Main Results:

• Achieved an excellent balance between image enhancement quality and computational efficiency.
• Demonstrated significant improvement in computational speed compared to existing methods.
• Validated the framework's effectiveness on a downstream segmentation task, enhancing surgical scene understanding.

Conclusions:

• The proposed framework offers a practical solution for real-time endoscopic image enhancement in clinical settings.
• Implicit degradation representation learning and inter-frame propagation are effective for efficient video enhancement.
• Enhanced endoscopic images improve surgical scene comprehension and facilitate clinical applications.