Enhancing Endoscopic Surgical Imaging: AI-Based Smoke Removal and Image Enhancement

The growing number of endoscopic surgery has underscored the critical need for accuracy and reliability in surgical imaging systems. However, endoscopic procedures face significant challenges, including limited lighting and smoke generated during surgeries. These factors obstruct visibility and compromise the precision of surgical interventions. This study focuses on developing an artificial intelligence-based system to enhance endoscopic image quality and effectively address these challenges. The proposed system integrates image enhancement algorithms with a smoke removal model. To mitigate low-light conditions and emphasize vascular structures, image enhancement was performed using various filters. For smoke removal, a generative adversarial network-based model was employed. Test results demonstrated that the system outperformed existing smoke-specific removal models, achieving PSNR and SSIM scores of 26.52 and 0.9119, respectively. These findings highlight the potential of this system to significantly improve the reliability and efficiency of endoscopic surgeries.