SwinIR is a novel image restoration model based on the Swin Transformer, designed to improve the quality of low-quality images. The model consists of three main components: shallow feature extraction, deep feature extraction, and high-quality image reconstruction. The deep feature extraction module uses residual Swin Transformer blocks (RSTBs), which combine Swin Transformer layers with residual connections to enhance long-range dependencies and content-based interactions. The shallow feature extraction module uses a convolutional layer to extract low-frequency information, while the reconstruction module fuses both shallow and deep features to produce high-quality images. Experimental results on various tasks, including image super-resolution, denoising, and JPEG compression artifact reduction, demonstrate that SwinIR outperforms state-of-the-art methods with significantly fewer parameters. The model's effectiveness is further validated through ablation studies and comparisons with CNN-based models, showing its efficiency and generalizability.SwinIR is a novel image restoration model based on the Swin Transformer, designed to improve the quality of low-quality images. The model consists of three main components: shallow feature extraction, deep feature extraction, and high-quality image reconstruction. The deep feature extraction module uses residual Swin Transformer blocks (RSTBs), which combine Swin Transformer layers with residual connections to enhance long-range dependencies and content-based interactions. The shallow feature extraction module uses a convolutional layer to extract low-frequency information, while the reconstruction module fuses both shallow and deep features to produce high-quality images. Experimental results on various tasks, including image super-resolution, denoising, and JPEG compression artifact reduction, demonstrate that SwinIR outperforms state-of-the-art methods with significantly fewer parameters. The model's effectiveness is further validated through ablation studies and comparisons with CNN-based models, showing its efficiency and generalizability.