基于GAN网络的红外图像生成技术

当将人工智能技术应用于军事领域中的目标识别任务时，针对由红外图片采集的局限性而造成的训练数据不足的问题，提出了基于生成对抗网络以生成红外图像的方法，实现了数据集的扩充。对基本的生成对抗网络进行了改进，将网络的输入由随机噪声变为真实图片，使之实现了图片到图片的风格转换，即彩色图片转变为红外图片。经过网络模型的搭建和训练，实验结果表明，该方法能够有效生成清晰和高质量的红外图片，解决了由红外数据不足而造成的网络训练不充分的问题。

GAN Based Technology of Infrared Image Generation

When artificial intelligence technology is used for target recognition in the military field, in order to solve the problem of insufficient training data caused by the limitations of infrared image collection, a method of infrared image generation based on a generative adversarial network is proposed to achieve the expansion of the dataset. The basic generative adversarial network is improved, and the input of the network is changed from random noise to real images, so that the style conversion from image to image is realized, i.e. the color image is converted into an infrared image. After the model construction and training of the network, the experimental results show that the method can effectively generate clear and high-quality infrared images, and solve the problem of insufficient network training caused by insufficient infrared data.