Cross-modal texture transformer for thermal infrared reference-based super-resolution reconstruction

Abstract

The Reference-based super-resolution(RefSR) methods add a reference image as an extra information source, which effectively resolves the issue of poor reconstruction accuracy resulting from the limited data in single-image super-resolution (SISR). However, the significant differences in thermal infrared and visible imaging mechanisms and image textures make it challenging to accomplish cross-modal texture matching and transfer. To address these issues, we propose a cross-modal texture transfer method for thermal infrared reference super-resolution reconstruction. First, we introduce a pre-trained feature encoder based on contrastive learning, which equips the encoder with the capability to match textures between thermal infrared and visible images. Next, to decrease computational complexity when matching features, we suggest a new sparse feature matching approach that regards areas of the image with abundant textures as suitable matching regions. Ultimately, we introduce a feature mapping module to identify differences in the distribution between infrared and visible images and to remap visible features to the infrared feature distribution. Extensive experiments demonstrate that our method effectively transfers textures from visible reference images to infrared images, resulting in superior visual effects and evaluation metrics.