Military equipment, such as infrared warning systems and infrared image-guided missiles, requires a number of infrared simulation images during performance evaluation and simulation training. However, the infrared simulation image generated by infrared simulation software has some problems, such as poor fidelity and poor universality. The domestic infrared simulation technology is hampered by foreign technology blockade. Currently, the development situation is difficult in meeting the requirement of the application. To solve this problem, this experiment proposed a visible-infrared image transform method. First, the region seeds growing (RSG) algorithm was used to extract the water target from the collected image. Subsequently, the visible and infrared image water target datasets were established. Finally, the trained cycle generative adversarial network (CycleGAN) was used to generate the infrared simulation image from the visible image. The test results show that the visual effect of the infrared simulation image generated by this method is close to that of the real infrared image and can be applied to the naval infrared military equipment simulation test and training system.