  We propose an effective lightweight dynamic local and global self-attention
network (DLGSANet) to solve image super-resolution. Our method explores the
properties of Transformers while having low computational costs. Motivated by
the network designs of Transformers, we develop a simple yet effective
multi-head dynamic local self-attention (MHDLSA) module to extract local
features efficiently. In addition, we note that existing Transformers usually
explore all similarities of the tokens between the queries and keys for the
feature aggregation. However, not all the tokens from the queries are relevant
to those in keys, using all the similarities does not effectively facilitate
the high-resolution image reconstruction. To overcome this problem, we develop
a sparse global self-attention (SparseGSA) module to select the most useful
similarity values so that the most useful global features can be better
utilized for the high-resolution image reconstruction. We develop a hybrid
dynamic-Transformer block(HDTB) that integrates the MHDLSA and SparseGSA for
both local and global feature exploration. To ease the network training, we
formulate the HDTBs into a residual hybrid dynamic-Transformer group (RHDTG).
By embedding the RHDTGs into an end-to-end trainable network, we show that our
proposed method has fewer network parameters and lower computational costs
while achieving competitive performance against state-of-the-art ones in terms
of accuracy. More information is available at
https://neonleexiang.github.io/DLGSANet/
