Abstract
SwinIR is a recent image restoration method based on the Swin Transformer architecture. In contrast to other traditional convolutional neural networks, SwinIR is capable of capturing sophisticated attention between image patches, leading to remarkable results. In this paper, we focus on the aspect of single-image super-resolution by SwinIR. We discuss the characteristics of the architecture of this algorithm and compare it to other deep learning methods. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source code is [[available here|https://github.com/JingyunLiang/SwinIR]] (last checked 2022/12/13).**
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