While some studies have proven that Swin Transformer (SwinT) with window
self-attention (WSA) is suitable for single image super-resolution (SR), SwinT
ignores the broad regions for reconstructing high-resolution images due to
window and shift size. In addition, many deep learning SR methods suffer from
intensive computations. To address these problems, we introduce the N-Gram
context to the image domain for the first time in history. We define N-Gram as
neighboring local windows in SwinT, 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 all outputs of the hierarchical encoder. Experimental results show that
NGswin achieves competitive performance while keeping an efficient structure,
compared with previous leading methods. Moreover, we also improve other
SwinT-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 will
be available soon.Comment: 8 pages (main content) + 14 pages (supplementary content