  Recently, deep learning has been successfully applied to the single-image
super-resolution (SISR) with remarkable performance. However, most existing
methods focus on building a more complex network with a large number of layers,
which can entail heavy computational costs and memory storage. To address this
problem, we present a lightweight Self-Calibrated Efficient Transformer (SCET)
network to solve this problem. The architecture of SCET mainly consists of the
self-calibrated module and efficient transformer block, where the
self-calibrated module adopts the pixel attention mechanism to extract image
features effectively. To further exploit the contextual information from
features, we employ an efficient transformer to help the network obtain similar
features over long distances and thus recover sufficient texture details. We
provide comprehensive results on different settings of the overall network. Our
proposed method achieves more remarkable performance than baseline methods. The
source code and pre-trained models are available at
https://github.com/AlexZou14/SCET.
