Infrared Image Generation Based on Visual State Space and Contrastive Learning

The preparation of infrared reference images is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared data on-site is difficult and time-consuming. Fortunately, the infrared images can be obtained from the corresponding visible-light images to enrich the infrared data. To this end, this present work proposes an image translation algorithm that converts visible-light images to infrared images. This algorithm, named V2IGAN, is founded on the visual state space attention module and multi-scale feature contrastive learning loss. Firstly, we introduce a visual state space attention module designed to sharpen the generative network's focus on critical regions within visible-light images. This enhancement not only improves feature extraction but also bolsters the generator's capacity to accurately model features, ultimately enhancing the quality of generated images. Furthermore, the method incorporates a multi-scale feature contrastive learning loss function, which serves to bolster the robustness of the model and refine the detail of the generated images. Experimental results show that the V2IGAN method outperforms existing typical infrared image generation techniques in both subjective visual assessments and objective metric evaluations. This suggests that the V2IGAN method is adept at enhancing the feature representation in images, refining the details of the generated infrared images, and yielding reliable, high-quality results.