Flow-based Gaussian Splatting for Continuous-Scale Remote Sensing Image Super-Resolution
High-resolution remote sensing images (RSIs) are crucial for Earth observation applications, yet acquiring them is often limited by sensor constraints and costs. In recent years, generative super-resolution (SR) methods, particularly diffusion models, have made significant progress. However, they typically require slow iterative inference with 40--1000 steps and exhibit limited flexibility in continuous-scale SR settings. To address these issues, we propose FlowGS, a generative reconstruction framework for arbitrary-scale SR of RSIs. FlowGS models the high-frequency detail representations between high- and low-resolution images and learns a continuous probability flow from noise to detail...
| Authors | Jiangwei Mo, Xi Lu, Hanlin Wu |
|---|---|
| Primary category | cs.CV |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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