https://github.com/xinntao/ESRGAN
New Updates.. We have extended ESRGAN to Real-ESRGAN, which is a more practical algorithm for real-world image restoration.For example, it can also remove annoying JPEG compression artifacts. You are recommended to have a try 😃. In the Real-ESRGAN repo,. You can still use the original ESRGAN model or your re-trained ESRGAN model.

https://arxiv.org/abs/1809.00219
ESRGAN is a paper that improves the visual quality of single image super-resolution by modifying the network architecture, adversarial loss and perceptual loss of SRGAN. The paper won the first place in the PIRM2018-SR Challenge and provides the code and models at a URL.

https://github.com/leverxgroup/esrgan
ESRGAN is a pipeline for image super-resolution based on a paper by Wang et al. It uses a deep convolutional neural network with residual-in-residual blocks and a discriminator network to generate high-resolution images from low-resolution ones.

https://esrgan.readthedocs.io/en/latest/index.html
ESRGAN is a deep convolutional neural network that reconstructs high-resolution images from low-resolution ones. Learn how to install, configure, and train ESRGAN with Catalyst, a framework for PyTorch-based pipelines.

https://www.tensorflow.org/hub/tutorials/image_enhancing
Learn how to use TensorFlow Hub Module for Enhanced Super Resolution Generative Adversarial Network (ESRGAN) to enhance bicubically downsampled images. See the code, results, and performance evaluation of the model trained on DIV2K Dataset.

https://github.com/xinntao/Real-ESRGAN
The ncnn implementation is in Real-ESRGAN-ncnn-vulkan; Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. 🌌 Thanks for your valuable feedbacks/suggestions.

https://link.springer.com/chapter/10.1007/978-3-030-11021-5_5
ESRGAN is a deep convolutional network for single image super-resolution that improves the visual quality over SRGAN. It uses a new network architecture, a relativistic discriminator and a perceptual loss to generate realistic textures and details.

https://arxiv.org/pdf/1809.00219
ESRGAN is a deep neural network approach for single image super-resolution that improves the visual quality and perceptual index of the results. The paper introduces the network architecture, loss functions and interpolation strategies of ESRGAN, and compares it with other methods on various datasets.

https://dl.acm.org/doi/abs/10.1007/978-3-030-11021-5_5
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Pages 63-79. Previous Chapter Next Chapter. Abstract. The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied

https://real-esrgan.com/
Real-ESRGAN is a state-of-the-art AI model that enhances and upscales images with unprecedented accuracy. It offers features such as standout face correction and customizable magnification ratios, and is ideal for improving compressed social media images.

https://huggingface.co/ai-forever/Real-ESRGAN
Real-ESRGAN. PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects. Real-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enhancing details while removing annoying

https://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Wang_ESRGAN_Enhanced_Super-Resolution_Generative_Adversarial_Networks_ECCVW_2018_paper.pdf
ESRGAN outperforms SRGAN in sharpness and details. high-frequency details, since the PSNR metric fundamentally disagrees with the subjective evaluation of human observers [25]. Several perceptual-driven methods have been proposed to improve the visual quality of SR results. For instance, perceptual loss [19,7] is proposed to optimize

https://medium.com/analytics-vidhya/esrgan-enhanced-super-resolution-generative-adversarial-network-using-keras-a34134b72b77
Learn how to use ESRGAN, a deep learning model that enhances the quality of images, with Keras. ESRGAN has a modified architecture, a relativistic discriminator, and a perceptual loss function.

https://paperswithcode.com/paper/esrgan-enhanced-super-resolution-generative
ESRGAN is a paper that improves the visual quality of single image super-resolution by modifying the network architecture, adversarial loss and perceptual loss of SRGAN. The paper won the first place in the PIRM2018-SR Challenge and provides code and results for comparison.

https://bytexd.com/how-to-use-esrgan-a-free-ai-tool-to-upscale-images/
Learn how to use ESRGAN, a free AI tool that can produce photorealistic images from low-resolution ones, on Google Colab. Follow the step-by-step guide to upload, run and download your upscaled images.

https://replicate.com/nightmareai/real-esrgan
Run Real-ESRGAN, a state-of-the-art image super-resolution model, with optional face correction and adjustable upscale using an API. See examples, readme, and billing information on Replicate.

https://ieeexplore.ieee.org/document/9054071
Abstract: Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is extended to further improve the perceptual quality of the images.

https://pyimagesearch.com/2022/06/13/enhanced-super-resolution-generative-adversarial-networks-esrgan/
Learn how to implement ESRGAN, a variant of SRGAN that improves super-resolution results and efficiency, using TensorFlow. The tutorial covers the architecture, loss functions, and training pipeline of ESRGAN with code examples and visualizations.

https://github.com/Lornatang/ESRGAN-PyTorch
Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge.

https://arxiv.org/abs/2001.08073
ESRGAN+ is a paper that proposes a novel block and noise inputs to enhance the perceptual quality of images generated by ESRGAN, a perceptual-driven approach for single image super resolution. The paper is submitted to ICASSP 2020 and the code is available online.

https://arxiv.org/abs/2107.10833
Real-ESRGAN is an extension of ESRGAN that can restore low-resolution images with unknown and complex degradations. It uses a high-order degradation modeling process, a U-Net discriminator and spectral normalization to improve visual performance.

https://arxiv.org/abs/2112.10046
A-ESRGAN is a generative adversarial network (GAN) model that restores low-resolution images with attention U-Net discriminators. It outperforms prior works on natural image quality evaluator metric and leverages image structural features in multiple scales.

https://github.com/xinntao/Real-ESRGAN/releases
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. - Releases · xinntao/Real-ESRGAN