Research on Cross-Sensor Remote Sensing Image Super-Resolution Method Based on Diffusion Models

In remote sensing image (RSI) super-resolution (SR), traditional deep learning methods have made remarkable progress. However, these methods struggle to handle the complex mapping between cross-sensor images, Although generative adversarial networks (GANs) can reconstruct fine details, their training process is relatively challenging. Recently, diffusionbased generative models have effectively improved the visual quality of reconstructed images through the iterative reverse diffusion mechanism. Therefore, this study proposes a crosssensor RSI SR method based on diffusion models, which aims to effectively reconstruct low-resolution (LR) RSI into highresolution (HR) images, addressing sensor differences and information loss. To extract rich prior information from LR images and improve image reconstruction, we designed a prior feature extraction module (PFEM), which aids in recovering HR features. Additionally, we designed an efficient sampling strategy that starts with LR images and progressively adds noise, replacing the traditional method that begins with pure noise. This approach leverages the LR image as an approximation of the intermediate state in the Markov chain, reducing the number of steps required by the diffusion model and improving generation efficiency. Finally, we used RSI from the Sentinel-2 (10m resolution) and Gaofen-2 (0.8m resolution) sensors to validate the reconstruction performance of the diffusion model. Experimental results confirm that the proposed method effectively utilizes LR priors for HR reconstruction while reducing computational costs.