Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly-applicable super-resolution framework called FunSR, which settles different magnifications with a unified model by exploiting context interaction within implicit function space. FunSR composes a functional representor, a functional interactor, and a functional parser. Specifically, the representor transforms the low-resolution image from Euclidean space to multi-scale pixel-wise function maps; the interactor enables pixel-wise function expression with global dependencies; and the parser, which is parameterized by the interactor's output, converts the discrete coordinates with additional attributes to RGB values. Extensive experimental results demonstrate that FunSR reports state-of-the-art performance on both fixed-magnification and continuous-magnification settings, meanwhile, it provides many friendly applications thanks to its unified nature.

翻译：尽管图像超分辨率在遥感领域取得了丰硕的应用成果，但因其需针对不同放大倍数分别训练独立模型，导致训练和部署过程较为繁琐。为此，我们提出一种高适用性的超分辨率框架FunSR，通过挖掘隐式函数空间内的上下文交互作用，以统一模型处理多种放大倍数。FunSR由功能表征器、功能交互器和功能解析器三部分组成。具体而言：表征器将低分辨率图像从欧几里得空间转换为多尺度逐像素函数映射；交互器使逐像素函数表达式具备全局依赖关系；而由交互器输出参数化的解析器，则将带有附加属性的离散坐标转换为RGB值。大量实验结果表明，FunSR在固定放大倍数和连续放大倍数设置下均达到业界领先性能，同时因其统一性架构提供了诸多便捷的应用场景。