Endoscopic videos are critical tools in minimally invasive medical procedures, enabling physicians to visualize internal organs in real time. However, these videos often suffer from visual artifacts, such as specular highlights, blur, bubbles, and instrument shadows, that can obscure critical anatomical features. Such artifacts not only hinder clinical interpretation but also pose challenges for downstream tasks such as computer-aided diagnosis, robotic navigation, and automated video analysis.<\/p>\n\n\n\n
These distortions can significantly affect the accuracy of diagnosis, training, and surgical planning, especially in high-stakes environments like bronchoscopy and urology. For instance, J&J\u2019s MONARCH\u2122 platforms rely heavily on high-quality visual data to enable precise robotic navigation and support various computer vision pipelines.<\/p>\n\n\n\n
To address this, our project focuses on enhancing endoscopic video quality using generative AI techniques<\/strong>. We explore state-of-the-art models for artifact detection, segmentation, and removal, aiming to restore image fidelity while preserving anatomical integrity. <\/p>\n\n\n\n Improving video quality in real-time and at high resolution introduces several challenges:<\/p>\n\n\n\n Our dataset and model target the following common artifact types:<\/p>\n\n\n\nTechnical Challenges<\/h2>\n\n\n\n
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Artifact Types<\/h2>\n\n\n\n
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Project Goals<\/h2>\n\n\n\n