  The Swin Transformer image super-resolution reconstruction network only
relies on the long-range relationship of window attention and shifted window
attention to explore features. This mechanism has two limitations. On the one
hand, it only focuses on global features while ignoring local features. On the
other hand, it is only concerned with spatial feature interactions while
ignoring channel features and channel interactions, thus limiting its
non-linear mapping ability. To address the above limitations, this paper
proposes enhanced Swin Transformer modules via alternating aggregation of
local-global features. In the local feature aggregation stage, we introduce a
shift convolution to realize the interaction between local spatial information
and channel information. Then, a block sparse global perception module is
introduced in the global feature aggregation stage. In this module, we
reorganize the spatial information first, then send the recombination
information into a dense layer to implement the global perception. After that,
a multi-scale self-attention module and a low-parameter residual channel
attention module are introduced to realize information aggregation at different
scales. Finally, the proposed network is validated on five publicly available
datasets. The experimental results show that the proposed network outperforms
the other state-of-the-art super-resolution networks.
