Real-ESRGAN is a deep learning model designed for real-world blind super-resolution using pure synthetic data. It extends the ESRGAN model to handle complex real-world image degradations by introducing a high-order degradation process that simulates multiple degradation steps, including blur, noise, resizing, and JPEG compression. The model also incorporates sinc filters to simulate common ringing and overshoot artifacts. A U-Net discriminator with spectral normalization is used to enhance the model's ability to distinguish real from synthetic images and stabilize training. Real-ESRGAN achieves superior visual performance on various real-world datasets compared to previous methods, effectively enhancing details while reducing artifacts. The model is trained using synthetic data generated through a second-order degradation process, which involves applying the classical degradation model multiple times. This approach allows Real-ESRGAN to handle complex real-world degradations more effectively than prior methods. The model also includes modifications such as the use of sinc filters and a U-Net discriminator to improve performance and stability. Real-ESRGAN demonstrates strong results in restoring realistic textures and reducing artifacts in real-world images, making it more practical for real-world applications. The model is trained on datasets such as DIV2K, Flickr2K, and OutdoorSceneTraining, and uses a combination of L1 loss, perceptual loss, and GAN loss for training. Real-ESRGAN outperforms other state-of-the-art methods in both artifact removal and texture restoration, particularly in handling complex degradations. The model also includes ablation studies to evaluate the effectiveness of different components, such as the second-order degradation process and the use of sinc filters. Despite its strengths, Real-ESRGAN has some limitations, including the potential for aliasing artifacts and the inability to handle out-of-distribution degradations. Overall, Real-ESRGAN represents a significant advancement in real-world blind super-resolution by effectively addressing complex degradations and improving the quality of restored images.Real-ESRGAN is a deep learning model designed for real-world blind super-resolution using pure synthetic data. It extends the ESRGAN model to handle complex real-world image degradations by introducing a high-order degradation process that simulates multiple degradation steps, including blur, noise, resizing, and JPEG compression. The model also incorporates sinc filters to simulate common ringing and overshoot artifacts. A U-Net discriminator with spectral normalization is used to enhance the model's ability to distinguish real from synthetic images and stabilize training. Real-ESRGAN achieves superior visual performance on various real-world datasets compared to previous methods, effectively enhancing details while reducing artifacts. The model is trained using synthetic data generated through a second-order degradation process, which involves applying the classical degradation model multiple times. This approach allows Real-ESRGAN to handle complex real-world degradations more effectively than prior methods. The model also includes modifications such as the use of sinc filters and a U-Net discriminator to improve performance and stability. Real-ESRGAN demonstrates strong results in restoring realistic textures and reducing artifacts in real-world images, making it more practical for real-world applications. The model is trained on datasets such as DIV2K, Flickr2K, and OutdoorSceneTraining, and uses a combination of L1 loss, perceptual loss, and GAN loss for training. Real-ESRGAN outperforms other state-of-the-art methods in both artifact removal and texture restoration, particularly in handling complex degradations. The model also includes ablation studies to evaluate the effectiveness of different components, such as the second-order degradation process and the use of sinc filters. Despite its strengths, Real-ESRGAN has some limitations, including the potential for aliasing artifacts and the inability to handle out-of-distribution degradations. Overall, Real-ESRGAN represents a significant advancement in real-world blind super-resolution by effectively addressing complex degradations and improving the quality of restored images.