Laparoscopy Surgery CO2 Removal via Generative Adversary Network and Dark Channel Prior (1909.12314v1)

Abstract: Laparoscopic surgery uses a thin tube with a camera called a laparoscope, which is inserted into the abdomen through a small incision in the skin during surgery. This allows to a surgeon to see inside of the body without causing significant injury to the patient. These characteristics make laparoscopy a widely used technique. In laparoscopic surgery, image quality can be severely degraded by surgical smoke caused by the use of tissue dissection tools which reduces the visibility of the observed organs and tissues. This lack of visibility increases the possibility of errors and surgery time with the consequences that this may have on the patient's health. In this paper, we introduce a novel hybrid approach for computational smoke removal which is based on the combination of a widely dehazing method used: the dark channel prior (DCP) and a pixel-to-pixel neural network approach: Generative Adversary Network (GAN). The experimental results have proven that the proposed method achieves a better performance than the individual results of the DCP and GAN in terms of restoration quality, obtaining a PSNR value of 25 and SSIM index of 0.88 over a test set of synthetic images.