Real‐time coloring method of laser surgery video based on generative adversarial network

Abstract

Lasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real-time colorization of the laser surgery video under black-andwhite imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real-time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736.