Remote Sensing Image Super-Resolution via Progressive Diffusion Schrödinger Bridge

Super-resolution (SR) of remote sensing images (RSIs) is essential for advanced image analysis, yet its progress is challenged by the ill-posed nature of SR and the geometric displacement errors commonly found in paired low-resolution (LR) and high-resolution (HR) training data. These displacements violate the assumptions of Gaussian diffusion models and restrict their effectiveness, especially when the scale gap between LR and HR images is large. To address these issues, we enhance the diffusion Schrödinger bridge (DSB) to exploit its ability to construct diffusion trajectories between arbitrary distributions and develop a progressive DSB (PDSB) framework that incrementally reconstructs HR images from their LR counterparts. The method divides the overall scale change into equal intervals so that small-scale SR results are first generated and then used as conditions for larger-scale reconstruction. Experiments conducted using a dataset built from georeferenced Gaofen-6 (2 m) and Sentinel-2 (10 m) images show that PDSB outperforms the comparison methods in commonly used metrics. Notably, the FID of PDSB is 8.294, which is half that of the second-place method. These results indicate that PDSB effectively mitigates displacement issues, enhances reconstruction accuracy, and demonstrates strong robustness and generalizability for practical RSI applications.