Lightweight Network for Single Image Super-Resolution with Arbitrary Scale Factor

Abstract

: The existing single image super-resolution (SISR) methods that consider integer scale factors (X2, X3, X4, and X8), have been developed well, but SISR methods with arbitrary scale factors (X1.3, X2.5, and X3.7) have gradually gained attention recently. Therefore, we proposed an efficient, lightweight model. In this study, there are two contributions as follows. (1) An efficient and lightweight network for SISR is combined with the up-scaled module, which determines its weights based on the size of the high-resolution (HR) image. (2) All scale factors are applied simultaneously using one model, which saves more storage and computational resources. Finally, we design various experiments to evaluate the proposed method based on multiple general datasets. The experimental results show that the proposed model is lightweight while the performance is relatively competitive.