Continuous Remote Sensing Image Super-Resolution Based on Context Interaction in Implicit Function Space

Keyan Chen, Wenyuan Li, Sen Lei, Jianqi Chen, Xiaolong Jiang, Zhengxia Zou, Zhenwei Shi

Abstract: Despite its fruitful applications in remote sensing, image super-resolution (SR) is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly applicable SR 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 multiscale pixelwise function maps; the interactor enables pixelwise 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 the state-of-the-art performance on both fixed- and continuous-magnification settings; meanwhile, it provides many friendly applications due to its unified nature. Our code is available at https://github.com/KyanChen/FunSR .