A Comparative Study of Generative and Diffusion Models for Specular Reflection Removal in Endoscopic Videos

Endoscopic video streams are crucial for guiding minimally invasive procedures and diagnostic analyses. However, the quality of these videos is often compromised by specular reflections, which obscure anatomical details. While traditional image processing methods struggle with the dynamic nature of these artifacts, deep learning approaches offer a promising solution. This paper presents a comprehensive evaluation of three state-of-the-art deep learning models for endoscopic reflection inpainting: Endo-STTN, DiffuEraser, and ProPainter. We analyze the distinct methodologies of each model, from the temporal consistency of Endo-STTN to the detailed synthesis capabilities of diffusion-based models. Our comparative analysis demonstrates that while all models improve image usability to varying degrees, they also present challenges, such as the potential for over-generation of details in diffusion models. Furthermore, we investigate the practical impact of these inpainting techniques on downstream clinical tasks, including polyp detection, segmentation, and depth estimation. The results indicate that reflection inpainting can significantly enhance the performance of these automated tasks, highlighting its value in clinical workflows.