  Recently, transformer-based methods have demonstrated impressive results in
various vision tasks, including image super-resolution (SR), by exploiting the
self-attention (SA) for feature extraction. However, the computation of SA in
most existing transformer based models is very expensive, while some employed
operations may be redundant for the SR task. This limits the range of SA
computation and consequently the SR performance. In this work, we propose an
efficient long-range attention network (ELAN) for image SR. Specifically, we
first employ shift convolution (shift-conv) to effectively extract the image
local structural information while maintaining the same level of complexity as
1x1 convolution, then propose a group-wise multi-scale self-attention (GMSA)
module, which calculates SA on non-overlapped groups of features using
different window sizes to exploit the long-range image dependency. A highly
efficient long-range attention block (ELAB) is then built by simply cascading
two shift-conv with a GMSA module, which is further accelerated by using a
shared attention mechanism. Without bells and whistles, our ELAN follows a
fairly simple design by sequentially cascading the ELABs. Extensive experiments
demonstrate that ELAN obtains even better results against the transformer-based
SR models but with significantly less complexity. The source code can be found
at https://github.com/xindongzhang/ELAN.
