Multi-scale implicit transformer with re-parameterization for arbitrary-scale super-resolution

Abstract

Methods based on implicit neural representations have recently exhibited excellent capabilities for arbitrary-scale super-resolution (ASSR). Although these methods represent the features of an image by generating latent codes, these latent codes are difficult to adapt to the different magnification factors of super-resolution (SR) imaging, seriously affecting their performance. To address this issue, we design a multi-scale implicit transformer (MSIT) that consists of a multi-scale neural operator (MSNO) and multi-scale self-attention (MSSA). MSNO obtains multi-scale latent codes through feature enhancement, multi-scale characteristic extraction, and multi-scale characteristic merging. MSSA further enhances the multi-scale characteristics of latent codes, resulting in improved performance. Furthermore, we propose the re-interaction module combined with a cumulative training strategy to improve the diversity of learned information for the network during training. We have systematically introduced multi-scale characteristics for the first time into ASSR. Extensive experiments are performed to validate the effectiveness of MSIT, and our method achieves state-of-the-art performance in ASSR tasks.