Style Transfer Meets Super-Resolution: Advancing Unpaired Infrared-to-Visible Image Translation with Detail Enhancement

The problem of unpaired infrared-to-visible image translation has gained significant attention due to its ability to generate visible images with color information from low-detail grayscale infrared inputs. However, current methodologies often depend on conventional style transfer techniques, which constrain the spatial resolution of the visible output to be equivalent to that of the input infrared image. The fixed generation pattern results in blurry generated results when translating low-resolution infrared inputs, and utilizing high-resolution infrared inputs as a solution necessitates greater computational resources. This spurs us to investigate the challenging unpaired image translation from low-resolution infrared inputs to high-resolution visible outputs, with the ultimate goal of enhancing image details while reducing computational costs. Therefore, we propose a unified framework that integrates the super-resolution process into our unpaired infrared-to-visible image transfer, yielding realistic and high-resolution results. Specifically, we propose the Detail Consistency Loss to establish a connection between the two aforementioned modules, thereby enhancing the quality of visual detail in style transfer results through the super-resolution module. Furthermore, our Texture Perceptual Loss is designed to ensure that the generator generates high-quality visual details accurately and reliably. Experimental results indicate that our method outperforms other comparative approaches when utilizing low-resolution infrared inputs. Remarkably, our approach even surpasses techniques that use high-resolution infrared inputs to generate visible images. Last but equally important, we propose a new and challenging dataset, dubbed as InfraredCity-HD, which comprises 512X512 resolution images, to advance research on high-resolution infrared-related fields.