  Image restoration is a long-standing low-level vision problem that aims to
restore high-quality images from low-quality images (e.g., downscaled, noisy
and compressed images). While state-of-the-art image restoration methods are
based on convolutional neural networks, few attempts have been made with
Transformers which show impressive performance on high-level vision tasks. In
this paper, we propose a strong baseline model SwinIR for image restoration
based on the Swin Transformer. SwinIR consists of three parts: shallow feature
extraction, deep feature extraction and high-quality image reconstruction. In
particular, the deep feature extraction module is composed of several residual
Swin Transformer blocks (RSTB), each of which has several Swin Transformer
layers together with a residual connection. We conduct experiments on three
representative tasks: image super-resolution (including classical, lightweight
and real-world image super-resolution), image denoising (including grayscale
and color image denoising) and JPEG compression artifact reduction.
Experimental results demonstrate that SwinIR outperforms state-of-the-art
methods on different tasks by $\textbf{up to 0.14$\sim$0.45dB}$, while the
total number of parameters can be reduced by $\textbf{up to 67%}$.
