Desmoking and Perceptual Quality Restoration of Laparoscopic Videos Using CycleGAN

Laparoscopic surgeries, valued for their minimally invasive nature, often suffer from visual obstructions caused by smoke from electrocauterization, impacting surgical precision and safety. This paper introduces a CycleGAN-based method for desmoking and restoring the perceptual quality of laparoscopic videos. Unlike traditional dehazing techniques, our approach uses inter-channel discrepancy and dark channel prior losses to effectively minimize residual smoke. Our key contributions include developing a novel GAN-based framework tailored for surgical video enhancement and introducing smoke-specific loss functions that improve visual clarity. Evaluated on the LVQ database, our model outperforms existing methods in image quality metrics and visual assessments. A user study with 25 surgeons confirmed its practical utility and potential for clinical integration, enhancing intraoperative imaging and surgical outcomes.