DroneSR: Rethinking Few-shot Thermal Image Super-Resolution from Drone-based Perspective

Although large-scale models achieve significant improvements in performance, the overfitting challenge still frequently undermines their generalization ability. In super-resolution tasks on images, diffusion models as representatives of generative models typically adopt large-scale architectures. However, few-shot drone-captured infrared training data frequently induce severe overfitting in large-scale architectures. To address this key challenge, our method proposes a new Gaussian quantization representation learning method oriented to diffusion models that alleviates overfitting and enhances robustness. At the same time, an effective monitoring mechanism tracks large-scale architectures during training to detect signs of overfitting. By introducing Gaussian quantization representation learning, our method effectively reduces overfitting while maintaining architecture complexity. On this basis, we construct a multisource drone-based infrared image benchmark dataset for detection and use it to emphasize overfitting issues of large-scale architectures in few sample, drone-based diverse drone-based image reconstruction scenarios. To verify the efficacy of the method in mitigating overfitting, experiments are conducted on the constructed benchmark. The experimental results demonstrate that our method outperforms existing super-resolution approaches and significantly mitigates overfitting of large-scale architectures under complex conditions. The code and DroneSR dataset will be available at: https://github.com/wengzp1/GARLSR