OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
This work provides a modular solution for researchers and practitioners in remote sensing to easily experiment with and deploy SRGAN models on diverse Earth-observation datasets, though it is incremental as it focuses on framework design rather than advancing state-of-the-art performance.
The authors tackled the challenge of applying super-resolution to multispectral Earth observation data by developing OpenSR-SRGAN, a flexible and open framework that simplifies configuration and deployment, resulting in a practical tool that lowers the entry barrier for researchers and practitioners.
We present OpenSR-SRGAN, an open and modular framework for single-image super-resolution in Earth Observation. The software provides a unified implementation of SRGAN-style models that is easy to configure, extend, and apply to multispectral satellite data such as Sentinel-2. Instead of requiring users to modify model code, OpenSR-SRGAN exposes generators, discriminators, loss functions, and training schedules through concise configuration files, making it straightforward to switch between architectures, scale factors, and band setups. The framework is designed as a practical tool and benchmark implementation rather than a state-of-the-art model. It ships with ready-to-use configurations for common remote sensing scenarios, sensible default settings for adversarial training, and built-in hooks for logging, validation, and large-scene inference. By turning GAN-based super-resolution into a configuration-driven workflow, OpenSR-SRGAN lowers the entry barrier for researchers and practitioners who wish to experiment with SRGANs, compare models in a reproducible way, and deploy super-resolution pipelines across diverse Earth-observation datasets.