SwinIR is an image restoration model based on the Swin Transformer, designed to achieve high-quality image restoration from low-quality inputs. 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 (RSTB), which combine Swin Transformer layers with residual connections to enhance performance. The model is tested on three tasks: image super-resolution (including classical, lightweight, and real-world), image denoising (grayscale and color), and JPEG compression artifact reduction. Experimental results show that SwinIR outperforms state-of-the-art methods by up to 0.45dB in PSNR while reducing the number of parameters by up to 67%. The model's effectiveness is attributed to its ability to capture long-range dependencies and content-based interactions, making it more efficient and effective than traditional CNN-based methods. SwinIR is also efficient in terms of computational complexity and parameter count, making it suitable for real-world applications. The model is evaluated on various benchmark datasets and demonstrates superior performance in image restoration tasks.SwinIR is an image restoration model based on the Swin Transformer, designed to achieve high-quality image restoration from low-quality inputs. 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 (RSTB), which combine Swin Transformer layers with residual connections to enhance performance. The model is tested on three tasks: image super-resolution (including classical, lightweight, and real-world), image denoising (grayscale and color), and JPEG compression artifact reduction. Experimental results show that SwinIR outperforms state-of-the-art methods by up to 0.45dB in PSNR while reducing the number of parameters by up to 67%. The model's effectiveness is attributed to its ability to capture long-range dependencies and content-based interactions, making it more efficient and effective than traditional CNN-based methods. SwinIR is also efficient in terms of computational complexity and parameter count, making it suitable for real-world applications. The model is evaluated on various benchmark datasets and demonstrates superior performance in image restoration tasks.