Abstract: The image smoke removal algorithm in medical operation can improve the imaging quality and reduce the harm of image-guided operation, which is a very ideal preprocessing method in many clinical applications. To solve this problem, this paper proposes an image smoke removal network based on conditional generation countermeasure model, which is composed of generator and discriminator subnetworks. Among them, the Tiramisu model is used instead of the traditional U-Net model to get higher parameter efficiency and performance. In addition, it provides a new way to generate a large number of training data sets for such problems by using the computer graphics rendering engine. The experimental results show that this method can effectively reduce the smoke while retaining the important perceptual information of the image, and is superior to the existing image smoke removal algorithms in both qualitative and quantitative analysis, thus providing a better visual field for surgeons.

Key words: laparoscopy, medical operation, deep learning, image desmoking, generative adversarial networks