  Recently, Transformer-based architecture has been introduced into single
image deraining task due to its advantage in modeling non-local information.
However, existing approaches tend to integrate global features based on a dense
self-attention strategy since it tend to uses all similarities of the tokens
between the queries and keys. In fact, this strategy leads to ignoring the most
relevant information and inducing blurry effect by the irrelevant
representations during the feature aggregation. To this end, this paper
proposes an effective image deraining Transformer with dynamic dual
self-attention (DDSA), which combines both dense and sparse attention
strategies to better facilitate clear image reconstruction. Specifically, we
only select the most useful similarity values based on top-k approximate
calculation to achieve sparse attention. In addition, we also develop a novel
spatial-enhanced feed-forward network (SEFN) to further obtain a more accurate
representation for achieving high-quality derained results. Extensive
experiments on benchmark datasets demonstrate the effectiveness of our proposed
method.
