OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data

TL;DR

OpenSR-SRGAN is a modular, configuration-based framework that simplifies applying GAN-based super-resolution to multispectral Earth observation data, facilitating experimentation, benchmarking, and deployment without extensive coding.

Contribution

It introduces an open, flexible, and easy-to-extend framework for super-resolution of satellite imagery, emphasizing configurability over state-of-the-art performance.

Findings

Provides a unified, configurable implementation of SRGAN-style models.

Enables easy switching between architectures, scales, and spectral bands.

Facilitates reproducible experiments and deployment in remote sensing applications.

Abstract

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.