Fourier Domain Adaptation for Thermal Image Super-Resolution

Thermal image super-resolution remains a challenging task due to the limited spatial detail captured by infrared sensors. Although RGB-guided methods and domain adaptation techniques have shown promise, they often introduce architectural complexity or require multimodal inputs at inference time. In this study, we investigate the integration of Fourier Domain Adaptation (FDA) as a lightweight preprocessing strategy to improve the performance of the state-of-the-art Dense-Residual-Connected Transformer (DRCT) model for image super-resolution. FDA transfers low-frequency information from grayscale versions of RGB images to thermal images during training, enabling the model to incorporate additional structural information without introducing any additional architectural complexity or adding overhead during inference. Experimental results on the Tufts Face Database demonstrate that the FDA-enhanced model improves PSNR by up to 0.17 dB and SSIM by up to 0.0004 compared to thermal-only training at a x2 scaling factor. These findings highlight the effectiveness of simple frequency-based adaptation techniques in improving the generalization of thermal SR models while preserving the simplicity of the inference pipeline.