  Implicit neural representation has recently shown a promising ability in
representing images with arbitrary resolutions. In this paper, we present a
Local Implicit Transformer (LIT), which integrates the attention mechanism and
frequency encoding technique into a local implicit image function. We design a
cross-scale local attention block to effectively aggregate local features. To
further improve representative power, we propose a Cascaded LIT (CLIT) that
exploits multi-scale features, along with a cumulative training strategy that
gradually increases the upsampling scales during training. We have conducted
extensive experiments to validate the effectiveness of these components and
analyze various training strategies. The qualitative and quantitative results
demonstrate that LIT and CLIT achieve favorable results and outperform the
prior works in arbitrary super-resolution tasks.
