  Transformer-based approaches have achieved superior performance in image
restoration, since they can model long-term dependencies well. However, the
limitation in capturing local information restricts their capacity to remove
degradations. While existing approaches attempt to mitigate this issue by
incorporating convolutional operations, the core component in Transformer,
i.e., self-attention, which serves as a low-pass filter, could unintentionally
dilute or even eliminate the acquired local patterns. In this paper, we propose
HIT, a simple yet effective High-frequency Injected Transformer for image
restoration. Specifically, we design a window-wise injection module (WIM),
which incorporates abundant high-frequency details into the feature map, to
provide reliable references for restoring high-quality images. We also develop
a bidirectional interaction module (BIM) to aggregate features at different
scales using a mutually reinforced paradigm, resulting in spatially and
contextually improved representations. In addition, we introduce a spatial
enhancement unit (SEU) to preserve essential spatial relationships that may be
lost due to the computations carried out across channel dimensions in the BIM.
Extensive experiments on 9 tasks (real noise, real rain streak, raindrop,
motion blur, moir\'e, shadow, snow, haze, and low-light condition) demonstrate
that HIT with linear computational complexity performs favorably against the
state-of-the-art methods. The source code and pre-trained models will be
available at https://github.com/joshyZhou/HIT.
