Resumen
Image generation technology is currently one of the popular directions in computer vision research, especially regarding infrared imaging, bearing critical applications in the military field. Existing algorithms for generating infrared images from visible images are usually weak in perceiving the salient regions of images and cannot effectively highlight the ability to generate texture details in infrared images, resulting in less texture details and poorer generated image quality. In this study, a cycle generative adversarial network method based on gradient normalization was proposed to address the current problems of poor infrared image generation, lack of texture detail and unstable models. First, to address the problem of limited feature extraction capability of the UNet generator network that makes the generated IR images blurred and of low quality, the use of the residual network with better feature extraction capability in the generator was employed to make the generated infrared images highly defined. Secondly, in order to solve issues concerning severe lack of detailed information in the generated infrared images, channel attention and spatial attention mechanisms were introduced into the ResNet with the attention mechanism used to weight the generated infrared image features in order to enhance feature perception of the prominent regions of the image, helping to generate image details. Finally, to tackle the problem where the current training models of adversarial generator networks are insufficiently stable, which leads to easy collapse of the model, a gradient normalization module was introduced in the discriminator network to stabilize the model and render it less prone to collapse during the training process. The experimental results on several datasets showed that the proposed method obtained satisfactory data in terms of objective evaluation metrics. Compared with the cycle generative adversarial network method, the proposed method in this work exhibited significant improvement in data validity on multiple datasets.