Efficient Infrared Image Generation Technology Based on Deep Learning

In existing infrared image generation technologies, challenges such as insufficient scene realism and low image generation efficiency are commonly encountered. To address these issues, this paper proposes an efficient infrared image generation technique based on deep learning, aimed at enhancing both the realism and efficiency of infrared images. This technique is built on an improved CycleGAN architecture, utilizing a U-Net structure as the generator and a convolutional neural network (CNN) as the discriminator. By introducing a diverse set of loss functions, we enhance the adversarial training of the generation network, allowing the generated infrared images to better resemble real scenes in terms of structure and detail. Furthermore, to improve image generation efficiency, we optimized the system's data transmission and display processing methods, achieving an image generation speed of up to 200 frames per second. The proposed technique not only effectively addresses multiple issues inherent in traditional infrared image generation but also provides robust technical support for the practical applications of infrared simulation systems, showcasing a broad range of application prospects