开源框架让卫星图像超分辨率配置更简单，支持多光谱数据
OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
- 通过配置文件管理模型结构与训练参数，无需修改代码
- 支持哨兵2号等多光谱数据，可灵活切换尺度与波段组合
- 提供开箱即用的配置和日志验证工具，适合科研与部署
我们提出 OpenSR-SRGAN，一个面向地球观测领域单图像超分辨率的开源模块化框架。该软件提供 SRGAN 风格模型的统一实现，便于配置、扩展和应用于哨兵2号等多光谱卫星数据。用户无需修改模型代码，即可通过简洁的配置文件切换生成器、判别器、损失函数和训练策略，支持不同架构、缩放因子和波段设置。框架定位为实用工具与基准实现，而非最先进模型，内置常见遥感场景的预设配置、合理的对抗训练默认参数，以及日志记录、验证和大场景推理的钩子功能。通过将基于 GAN 的超分辨率转化为配置驱动的工作流，OpenSR-SRGAN 降低了研究者与实践者实验、比较模型及部署跨数据集超分辨率流水线的门槛。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。