Multi-task learning based approach for surgical video desmoking.

Over the past few decades, minimally invasive surgical techniques have gained wide acceptance due to multiple benefits it offers. In these surgeries, a camera with a light source is inserted via a small incision. The video feed from the camera is the only source for visualization of internal organs. Certain procedures produce fumes that severely degrade the video feed. Various image processing based de-smoking systems are proposed to provide a continuous, good quality video feed. However, most of the existing approaches perform de-smoking at the frame level and fail to exploit the dynamic properties of the smoke. We propose a novel de-smoking model that harnesses both spatial and temporal properties of smoke. We evaluate the performance of the proposed model on the Cholec-80 dataset and observe a superior performance in terms of MS-SSIM and PSNR metrics compared to existing works.