  Recently, Transformer-based methods have achieved impressive results in
single image super-resolution (SISR). However, the lack of locality mechanism
and high complexity limit their application in the field of super-resolution
(SR). To solve these problems, we propose a new method, Efficient Mixed
Transformer (EMT) in this study. Specifically, we propose the Mixed Transformer
Block (MTB), consisting of multiple consecutive transformer layers, in some of
which the Pixel Mixer (PM) is used to replace the Self-Attention (SA). PM can
enhance the local knowledge aggregation with pixel shifting operations. At the
same time, no additional complexity is introduced as PM has no parameters and
floating-point operations. Moreover, we employ striped window for SA (SWSA) to
gain an efficient global dependency modelling by utilizing image anisotropy.
Experimental results show that EMT outperforms the existing methods on
benchmark dataset and achieved state-of-the-art performance. The Code is
available at https://github.com/Fried-Rice-Lab/FriedRiceLab.
