  Previous works have shown that increasing the window size for
Transformer-based image super-resolution models (e.g., SwinIR) can
significantly improve the model performance but the computation overhead is
also considerable. In this paper, we present SRFormer, a simple but novel
method that can enjoy the benefit of large window self-attention but introduces
even less computational burden. The core of our SRFormer is the permuted
self-attention (PSA), which strikes an appropriate balance between the channel
and spatial information for self-attention. Our PSA is simple and can be easily
applied to existing super-resolution networks based on window self-attention.
Without any bells and whistles, we show that our SRFormer achieves a 33.86dB
PSNR score on the Urban100 dataset, which is 0.46dB higher than that of SwinIR
but uses fewer parameters and computations. We hope our simple and effective
approach can serve as a useful tool for future research in super-resolution
model design.
