Real esrgan paper pdf

Real esrgan paper pdf. Fewer artifacts. The findings from this investigation unequivocally demonstrate that conducting object detection on the SR image not only results in a notable increase in the quantity of detected objects but also leads to a significant enhancement in the overall accuracy of the detection process. Mar 28, 2021 · The paper proposes the following techniques: Improves the model architecture using RRDB (Residual-in-residual Dense Block) without batch normalization based on the observations of EDSR [4]. To improve performance, ESRGAN adopted the basic architecture of SRResNet, in which Residual-in-Residual Dense Blocks are substituted for the traditional ESRGAN basic blocks, as shown in Figure 5. However, because of its complexity and higher visual requirements of medical images, SR is still a challenging task in medical imaging. In simple terms, it uses artificial intelligence to The ncnn implementation is in Real-ESRGAN-ncnn-vulkan; Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. The accuracy of the number plate extraction is an important thing that must be considered. original image, bicubic downsampled image and recovered image from downsampling. Considered baseline state-of-the-art methods: ESRGAN, DAN, CDC, RealSR, and BSRGAN. Real-ESRGAN. Real-ESRGAN is an advanced ESRGAN-based super-resolution tool trained on synthetic data to enhance image details and reduce noise. This was May 15, 2020 · Higher PI, lower perceptual quality. Mar 8, 2024 · Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. ‘×2’ on pixel shuffle module means it Jan 2, 2023 · You can find more information here. CV] Google Scholar Jul 10, 2021 · plot_image(tf. Jul 22, 2021 · #3 best model for Video Super-Resolution on MSU Video Upscalers: Quality Enhancement (LPIPS metric) Recently, several Real-ISR methods such as BSRGAN [13], Real-ESRGAN [29] and SwinIR [36] have achieved remarkable progress by introducing comprehen-sive degradation models to e ectively synthesize real-world images. better texture restoration. arxiv:1809. In this fashion, the model is extended to further improve the perceptual quality of the images. The focus of this paper is on enhancing the resolution and perceptual quality of chest X-ray and retinal images. pickle. A super-resolution enhancement method for single-frame infrared images based on improved Real-ESRGAN to resolve the problem of low resolution and lack of detailed texture of infrared images so that it can improve the accuracy of object detection. Despite the visual quality of these generated images, there is still room for improvement. Dec 12, 2022 · In Fig. The above analytical results are Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. In this ️ Real-ESRGAN: A practical algorithm for general image restoration ️ GFPGAN: A practical algorithm for real-world face restoration ️ facexlib: A collection that provides useful face-relation functions. Specifically, a high-order degradation Real-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enhancing details while removing annoying artifacts for common real-world images. The aim of blind super-resolution (SR) in computer vision is to improve the resolution of an image without prior Dec 31, 2022 · (DOI: 10. 67. Various models like Dense net, Mobile net Real-ESRGAN with optional face correction and adjustable upscale. The advent of Deep learning models led to an unprecedented change in the field of medical image analysis. However, the images reconstructed by Real-ESRGAN suffer from two notable weaknesses Dec 19, 2021 · In this paper, we present A-ESRGAN, a GAN model for blind SR tasks featuring an attention U-Net based, multi-scale discriminator that can be seamlessly integrated with other generators. Ideal for improving compressed social media images. Specifically, a high-order degradation modeling process is introduced to better simulate complex real-world degradations. more faithful to the original colors. Public. Note that you can also access the model on Github if you'd like to further dive into the details. No virus. a86fc61 about 2 years ago. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. - "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data" Nov 1, 2022 · In our proposed approach, the pre-trained generator and discriminator networks of the Real-ESRGAN model are fine-tuned using medical image datasets. high-frequency details, since the PSNR metric fundamentally disagrees with the subjective evaluation of human observers [25]. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. Edit social preview. However, the In this paper, we train the practical Real-ESRGAN for real-world blind super-resolution with pure synthetic training pairs. For the scale factor of ×2 and ×1, it first employs a pixel-unshuffle operation to reduce spatial size and re-arrange information to the channel dimension. It's specifically designed to upscale images while maintaining (or even enhancing) their quality. The generator is to create fake images while the discriminator judges them as real or fake. This work extends the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. 00219v2 [cs. RRDB is used as Basic Block in ESRGAN. 9M runs. The taking into account of the contours allows a better blur reduction on the HR image on the informative zones, with more details. Oct 23, 2023 · The discriminator network model in this paper is based on the Real-ESRGAN model and adopts a U-net discriminator with instance normalization . Paper: Real Jan 23, 2019 · Hence, this paper introduces a new network called PANet-UP-ESRGAN (PAUP-ESRGAN), specifically designed to obtain low-dose CT (LDCT) images with high peak signal-to-noise ratio (PSNR) and high May 13, 2020 · Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI). To optimize and evaluate the performance of the model, we use the USR-248 dataset. In order to synthesize more practical degradations, we propose a high-order degradation process and employ s i n c 𝑠 𝑖 𝑛 𝑐 sinc filters to model common ringing and overshoot artifacts. g. Jan 10, 2024 · $ python3 real_esrgan. 2023. In this paper, we optimize the Real-ESRGAN model for underwater image super-resolution. 2. A detailed guide can be found in Training. Related Work The image super-resolution field [21,24,45,17,25,27, 58,22,44,57,7,30] has witnessed a variety of develop- Apr 1, 2023 · In Section 4, we integrate DP Loss into SRGAN for hyperparameter analysis, and apply the loss function under the optimal hyperparameter combination to ESRGAN to obtain ESRGAN-DP, and then compare ESRGAN-DP with other state-of-the-art SR methods to verify the effectiveness of the proposed method; Section 5 concludes this paper. Paper (Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data) Original implementation; Huggingface 🤗 This project is the ncnn implementation of Real-ESRGAN. Simply put, it's your low-resolution input image. , RRDB [2] and Swin transformer [37], and are not Zoom in for best view - "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data" Search 216,152,161 papers from all fields of science The research aims to propose an efficient approach for detecting and decoding QR codes from unclear images using the You Only Look Once (YOLO) object detection model and deep super-resolution techniques implemented through Real-ESRGAN. Motivation of Practical Degradation Modelling Classical degradation model Complicated combinations of degradation Pipeine for Image Super-Resolution task that based on a frequently cited paper, ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (Wang Xintao et al. Related Work The image super-resolution field [19,22,39,15,23,25, 51,20,38,50,6,28] has witnessed a variety of develop- arXiv:1809. Context and perceptual losses are used for proper image upscaling, while adversarial loss pushes neural network to the natural image manifold using a Feb 19, 2022 · Python. Playground API Examples README Versions. • Real-ESRGAN trained with pure synthetic data is able to restore most real-world images and achieve better visual performance than previous works, making it more practical in real-world applications. boomb0om. Unmanned Aerial Vehicles (UAVs), commonly known as drones, in recent years continue to gain popularity in various RealESRGAN high order degradation pipeline. arxiv:2107. The generator produces super-resolution images, while the discriminator judges them as real and fake. 画像系の機械学習の分野の1つである「超解像」について初心者向けに紹介します。. Features standout face correction and customizable magnification ratios. better background restoration. QR codes have become increasingly popular in various applications, such as inventory management, advertising, and payment systems. Dec 19, 2021 · Download a PDF of the paper titled A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators, by Zihao Wei and 4 other authors Download PDF Abstract: Blind image super-resolution(SR) is a long-standing task in CV that aims to restore low-resolution images suffering from unknown and complex distortions. To address this problem, we propose using an additional perceptual loss (computed using the pretrained PieAPP network) for Jul 20, 2023 · Real-ESRGAN synthesizes paired data with operations including blur, noise, and generalized trained models to real degradation. In our proposed Jul 22, 2021 · Figure 5: Top: Real samples suffering from ringing and overshoot artifacts. ️ HandyFigure: Open source of paper figures Pipeine for Image Super-Resolution task that based on a frequently cited paper, ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (Wang Xintao et al. IV] Google Scholar; Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaoou Tang. SRGAN ESRGAN Ground Truth Fig. Total loss for the Generator is calculated as The ncnn implementation is in Real-ESRGAN-ncnn-vulkan; Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. The default value is 4, but you can adjust it as per your needs. All the feedbacks are updated in feedback. However, they rely on a heavy and computationally intensive backbone network, e. 9, the probability density function (PDF) of each velocity component is plotted as a function of the wall-normal location to evaluate the capability of the 3D-ESRGAN model to reconstruct velocity fields with accurate spatial distributions. Run with an API. CV] 17 Sep 2018. Scale number: This is the factor by which you want to scale your image. Jul 30, 2021 · Real-ESRGAN is trained on 256x256 patches from DIV2K, Flickr2K, and OutdoorSceneTraining datasets. start = time. Uses Jul 18, 2023 · Download a PDF of the paper titled A comparative analysis of SRGAN models, by Fatemeh Rezapoor Nikroo and 6 other authors Download PDF Abstract: In this study, we evaluate the performance of multiple state-of-the-art SRGAN (Super Resolution Generative Adversarial Network) models, ESRGAN, Real-ESRGAN and EDSR, on a benchmark dataset of real This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN. Face Enhance: A boolean value (true/false). The first part is training Real-ESRNet (Wang et al, 2021) with the L1 loss. Jun 13, 2022 · GANs train two neural networks: the discriminator and the generator, simultaneously. Real-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enhancing details while removing annoying artifacts for common real-world images. For each convolutional layer, channel (c) and strides (s) are pointed out above. It is fine-tuned from ESRGAN with a batch size of 48 for 500k iterations with L1, perceptual, and GAN losses. Overview: While there are many blind image restoration approaches, few can handle complex real-world degradations. Intensity differences between images can be rather large Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. Real-ESRGAN ncnn Vulkan heavily borrows from realsr-ncnn-vulkan. Hence, this paper introduces a new network called PANet-UP-ESRGAN (PAUP-ESRGAN Mar 15, 2023 · ESRGAN (shown in Figure 5) pictures can more closely mimic image artifacts’ sharp edges . The baselines are nowhere near Real-ESRGAN in This project is the implementation of the Real-ESRGAN and the Real-ESRNet models from the paper "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data" Problem Statement Trying to improve the quality of blurry images without knowing how they got blurry in the first place. Jul 22, 2021 · 22 Jul 2021 · Xintao Wang , Liangbin Xie , Chao Dong , Ying Shan ·. g Jul 1, 2023 · StarSRGAN is introduced, a novel GAN model designed for blind super-resolution tasks that utilize 5 various architectures that provides new SOTA performance with roughly 10% better on the MANIQA and AHIQ measures, as demonstrated by experimental comparisons with Real-ESRGAN. The High-order Deterioration Model(HDM) of Real-ESRGAN is more effective than the conventional bicubic kernel interpolation in simulating the degradation of real-world images. Jan 17, 2022 · A effective method called Dual Perceptual Loss (DP Loss), which is used to replace the original perceptual loss to solve the problem of single image super-resolution reconstruction, and considers the advantages of learning two features simultaneously, which significantly improves the reconstruction effect of images. 今回はReal-ESRGANの公式チュートリアルに沿って実装する方法を紹介します。. GitHub. For instance, perceptual loss [13,14] is proposed to opti- Jan 21, 2020 · Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super resolution that is able to produce photorealistic images. Sep 1, 2018 · The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. Key points of ESRGAN: SRResNet-based architecture with residual-in-residual blocks; Mixture of context, perceptual, and adversarial losses. To further enhance the visual quality, we thoroughly study three key components of SRGAN SRGAN ESRGAN Ground Truth Fig. The authors also consider the common ringing and overshoot artifacts in the Nov 22, 2022 · Three CNN models Mobile net, Nas net, and Dense net are combined with Real ESRGAN and their classification accuracy enhances above 90% and images which were not correctly classified with base models, are now classified with near to one probability. Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired Jan 21, 2020 · Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super resolution that is able to produce photorealistic images. 1 MB. To our knowledge, this is the first work to introduce attention U-Net structure as the discriminator of GAN to solve blind SR problems. Contribute to liux520/RealESRGAN_Deg_Pipeline development by creating an account on GitHub. From the paper. ESRGAN stands for Enhanced Super-Resolution Generative Adversarial Network. Indeed, it is a blind super-resolution with pure synthetic training May 25, 2020 · Similar to SRGAN, ESRGAN also scales the Low Resolution(LR) image to High Resolution(HR) image from 64 x 64 to 256 x 256 with up-scaling factor of 4. ️ HandyView: A PyQt5-based image viewer that is handy for view and comparison. We also optimize it for anime images. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration. ESRGAN. 38. To fine-tune and evaluate the performance of the model, we use the USR-248 dataset. We have designed a novel block dynamics. To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. Real-ESRGAN with optional face correction and adjustable upscale. The proposal of perceptual loss solves the problem that per-pixel difference A deep learning based method called Medical Images SR using Generative Adversarial Networks (MedSRGAN) for SR in medical imaging showed that MedSRGAN not only preserves more texture details but also generates more realistic patterns on reconstructed SR images. 3256086) Image degradation technique has been the focus of current research in image super-resolution( SR). It is also easier to integrate this model into your projects. This is not an official implementation. 1: The super-resolution results of 24 for SRGAN , the proposed ESRGAN and the ground-truth. インターネット上の画像に超解像を適用しようとした場合、元画像にはノイズが含まれているため、リサイズによる Generative Adversarial Network) models, ESRGAN, Real-ESRGAN and EDSR, on a benchmark dataset of real-world images which undergo degradation using a pipeline. The rest of this paper is organized as follows. We partially use code from the original repository. Exploring real-time super-resolution generative adversarial networks Image super-resolution is an essential technology for improving user quality of experience of internet videos. Zoom in for best view - "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data" Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. Perceptual quality is a quality which is much close to human eyes. squeeze(lr_image), title="Low Resolution") Perform the super-resolution and showcase the output side by side i. In few words, image super-resolution (SR) techniques reconstruct a higher-resolution (HR) image or sequence from the observed lower-resolution (LR) images, e. Jul 16, 2022 · Spatial resolution of medical images can be improved using super-resolution methods. In this paper, we apply this method to enhance the spatial resolution of 2D MR images. In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. 2. 10833 [eess. md. 2018. 🌌 Thanks for your valuable feedbacks/suggestions. Fig. Update model card files. 2 Xintao Wang et al. Bottom: Examples of sinc kernels (kernel size 21) and the corresponding filtered images. According to the Real-ESRGAN paper the training process is divided into two stages. The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating Oct 1, 2021 · Therefore, in this paper, we choose to use the Real-ESRGAN (Wang et al. In this work, we fine-tune the pre-trained Real-ESRGAN model with medical image datasets so that it performs better in enhancing the resolution and quality of medical images. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images, given input images of lower resolution. g Figure 2: Overview of the pure synthetic data generation adopted in Real-ESRGAN. ESRGAN outperforms SRGAN in sharpness and details. This technique helps in improving the performance of the model. Jul 22, 2021 · Figure 4: Real-ESRGAN adopts the same generator network as that in ESRGAN. 実際に解像度の低い Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. 5. However, detecting and Real-ESRGAN / RealESRGAN_x2. Sep 20, 2022 · Real-ESRGAN V0. We also employ sinc filter to synthesize common ringing and overshoot artifacts May 27, 2023 · Inputs. It utilizes a second-order degradation process to model more practical degradations, where each degradation process adopts the classical degradation model. However, the hallucinated details are often accompanied with unpleasant artifacts. 00219 [cs. As the state-of-the-art deep learning-based super-resolution technology, the enhanced super-resolution generative adversarial networks (ESRGAN) Mar 18, 2023 · In this paper, we have proposed a new SR method, called Bi-ESRGAN, more adapted for the SR of document image, with the aim of improving the existing ESRGAN method based on Transfer Learning and edge detection. download history blame contribute delete. ), published in 2018. jpg -s output. nightmareai / real-esrgan Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. 【はじめての超解像】Real-ESRGANを使って画像を高解像度化してみる. SRGAN ESRGAN Ground Truth. 1: The super-resolution results of ×4 for SRGAN, the proposed ESRGAN and the ground-truth. nightmareai / real-esrgan. This file is stored with Git LFS . ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Reducing the radiation dose may lead to scattering noise and low resolution, which can adversely affect the radiologists’ judgment. Jan 1, 2023 · The High-order Deterioration Model (HDM) of Real-ESRGAN is more effective than the conventional bicubic kernel interpolation in simulating the degradation of real-world images. We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. Jan 23, 2019 · The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. This result owes to the combination of attention mechanism and U-Net Structure in our proposed discriminator. 🚩 The training codes have been released. May 27, 2023 · The Real-ESRGAN model, created by the skilled NightmareAI, is a marvel in the world of image-to-image conversion. However, the hallucinated Jul 29, 2022 · ESRGAN is a generative adversarial network that produces visually pleasing super-resolution (SR) images with high perceptual quality from low-resolution images. Several perceptual-driven methods have been proposed to improve the visual quality of SR results. The detailed choices for blur , resize, noise and JPEG compression are listed. Our model shows superiority over the state-of-the-art real-ESRGAN model in sharpness and details (see 8b). time() fake_image = model(lr_image) Nov 7, 2022 · Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) is an efficient model that has shown remarkable performance among SISR models. 3) Real-ESRGAN trained with pure synthetic data is able to restore most real-world images and achieve better visual performance than previous works, making it more practical in real-world applications. In terms of cases 1 and 2, the PDF plots of the reconstructed velocity components agree The accuracy of the number plate extraction is an important thing that must be considered and the efforts to reduce the most optimal CER value could be done by upscaling utilizing Real-ESRGAN, especially by operating anime mode which had been done with grayscaling and thresholding processes. e. The major update: 🎉. Sep 15, 2016 · In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). pth. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network CVF Open Access Jul 22, 2021 · Download a PDF of the paper titled Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, by Xintao Wang and 3 other authors Download PDF Abstract: Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing The ncnn implementation is in Real-ESRGAN-ncnn-vulkan; Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration. ESRGAN intends to improve perceptual quality rather than objective quality, such as PSNR. License. SRGANs used this idea in the domain of image super-resolution. , 2021) algorithm to improve the clarity of suspected fire areas in remotely sensed images. By default, Real ESRGAN is optimized for real-world images, but it also supports an anime model which can be activated by using the -m . U-Net Structure in discriminator can provide per-pixel feedback to the generator[6], which can help the generator to generate And ESRGAN (Enhanced SRGAN) is one of them. We have designed a network dynamics. Jul 25, 2023 · The following descriptions are based on the Real-ESRGAN paper on Arxiv, uploaded by the creator Xinntao. The improvements are: better naturalness. This model shows better results on faces compared to the original version. Aug 1, 2020 · The generator structure Residual Whole Map Attention Network (RWMAN). Our results show that some models seem to significantly increase the resolution of the input images while preserving their visual quality, this is assessed using Tesseract OCR engine. Sep 7, 2023 · Real ESRGANのアーキテクチャ. 1109/access. Real-ESRGAN accepts two types of inputs: Image file: This is the image you want to enhance. Explore Pricing Docs Blog Changelog Sign in Get started. When they trained Real-ESRGAN, they used the weights from the Real-ESRNet as the initialization point. The proposed model generates images that demonstrate a higher level of visual quality than the outputs of the Real-ESRGAN model. However, it frequently fails to recover local details, resulting in blurry or unnatural visual artifacts. 0 Release Note. In this paper, we fine-tune the pre-trained Real-ESRGAN model for underwater image super-resolution. You can try it in google colab. Residual-in-Residual Dense Block (RRDB) The basic architecture of SRResNet/SRGAN. Due to the defects such as low resolution, lack of hierarchy, and blurred visual effects in infrared images, the accuracy of detecting infrared Real-ESRGAN. Many thanks to nihui, ncnn and realsr-ncnn-vulkan 😁. PyTorch implementation of a Real-ESRGAN model trained on custom dataset. In our experiment, we started our training by Dec 8, 2023 · The popularization and widespread use of computed tomography (CT) in the field of medicine evocated public attention to the potential radiation exposure endured by patients. We update the RealESRGAN AnimeVideo-v3 model, which can achieve better results with a faster inference speed. py -i input_anime. In this paper, we worked on retinal images and These reasons motivated us to modify the Real-ESRGAN model for use in the field of medical image super-resolution. Compared with the VGG-style discriminator, U-net has skip connections and outputs the true values of each pixel in the image, which can provide detailed per-pixel feedback to the generator. Yet Real-ESRGAN by Xintao Wang and his colleagues from ARC, Tencent PCG, Shenzen Institutes, and University of Chinese Academy of Sciences takes real-world image super-resolution (SR) to the next level! Nov 1, 2022 · In our proposed approach, we use transfer learning technique and fine-tune the pre-trained Real-ESRGAN model using medical image datasets. Comparison of super-resolution outputs by real-ESRGAN vs ESRGAN - note the improvement in real-world images. Get Started for Free. jpg. iz gl qx xu kt vh cc lh rx jc