  Thermal imaging plays a crucial role in various applications, but the
inherent low resolution of commonly available infrared (IR) cameras limits its
effectiveness. Conventional super-resolution (SR) methods often struggle with
thermal images due to their lack of high-frequency details. Guided SR leverages
information from a high-resolution image, typically in the visible spectrum, to
enhance the reconstruction of a high-res IR image from the low-res input.
Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on
Swin transformers. In real world scenarios, however, the guiding modality (e.g.
RBG image) may be missing, so we propose a training method that improves the
robustness of the model in this case. Our method has few parameters and
outperforms state of the art models in terms of Peak Signal to Noise Ratio
(PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal
Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our
code and pretained weights are available at
https://github.com/VisionICLab/SwinFuSR.
