  While some studies have proven that Swin Transformer (Swin) with window
self-attention (WSA) is suitable for single image super-resolution (SR), the
plain WSA ignores the broad regions when reconstructing high-resolution images
due to a limited receptive field. In addition, many deep learning SR methods
suffer from intensive computations. To address these problems, we introduce the
N-Gram context to the low-level vision with Transformers for the first time. We
define N-Gram as neighboring local windows in Swin, which differs from text
analysis that views N-Gram as consecutive characters or words. N-Grams interact
with each other by sliding-WSA, expanding the regions seen to restore degraded
pixels. Using the N-Gram context, we propose NGswin, an efficient SR network
with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder.
Experimental results show that NGswin achieves competitive performance while
maintaining an efficient structure when compared with previous leading methods.
Moreover, we also improve other Swin-based SR methods with the N-Gram context,
thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG
outperforms the current best lightweight SR approaches and establishes
state-of-the-art results. Codes are available at
https://github.com/rami0205/NGramSwin.
