关键词: 图像到图像转换, 语义对比学习, 红外图像生成, 多尺度判别器, 几何一致性约束

Abstract: Infrared images are widely used in computer vision,but high-quality infrared image datasets are limited in scale due to restricted acquisition conditions.To address this problem,converting visible datasets to infrared datasets has become an effective way.Existing generation methods generally rely on supervised learning,which requires a large amount of paired data that is extremely difficult to obtain in practical applications.This paper proposes an unsupervised infrared image generation method named DSCGAN.This method adopts a bidirectional transformation architecture and introduces semantic contrast learning to enhance the ability to preserve image content and learn discriminative infrared features.The geometric consistency loss is introduced to preserve the original structure and details of visible images effectively.Meanwhile,a multi-scale PatchGAN discriminator is constructed to improve discriminative capability and enhance the realism of generated images.Experimental results on the AVIID-1,AVIID-2,and Day-DroneVehicle datasets show that DSCGAN outperforms the comparison methods in several metrics,and the generated infrared images exhibit a more reasonable thermal radiation distribution and better visual quality.In the AVIID-1 dataset,the SSIM value increases to 0.814 4,and the FID score decreases to 0.145 6.In the Day-DroneVehicle dataset,the PSNR value improves to 18.14,while the LPIPS value drops to 0.294 9.This study provides a new idea for unsupervised infrared image gene-ration,with potential applications in infrared target detection,infrared scene segmentation,and other downstream tasks.

Key words: Image-to-image translation, Semantic contrastive learning, Infrared image generation, Multi-scale discriminator, Geometric consistency constraint