Log in

Relevant bibliographies by topics / ESRGAN / Journal articles

To see the other types of publications on this topic, follow the link: ESRGAN.

Journal articles on the topic 'ESRGAN'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'ESRGAN.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Rifai, Rochan, Handi Putra Utama, Fikhri Astina Tasmara, et al. "Enhancing resolution of raster-scan photoacoustic imaging using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN)." Journal of Physics: Conference Series 2945, no. 1 (2025): 012040. https://doi.org/10.1088/1742-6596/2945/1/012040.

Full text

Abstract:

Abstract Raster-scan photoacoustic imaging often faces challenges with low resolution and extended acquisition times, limiting its effectiveness in biomedical applications. Traditional interpolation methods, such as nearest-neighbor, Bilinear, and Bicubic, do not fully address these issues, resulting in residual blurring and artifacts. This study investigates the use of Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to enhance the resolution of raster-scan photoacoustic images. We compare ESRGAN with conventional interpolation techniques to assess improvements in image qual

APA, Harvard, Vancouver, ISO, and other styles

2

Sun, Zhonghua, and Curtise K. C. Ng. "Finetuned Super-Resolution Generative Adversarial Network (Artificial Intelligence) Model for Calcium Deblooming in Coronary Computed Tomography Angiography." Journal of Personalized Medicine 12, no. 9 (2022): 1354. http://dx.doi.org/10.3390/jpm12091354.

Full text

Abstract:

The purpose of this study was to finetune a deep learning model, real-enhanced super-resolution generative adversarial network (Real-ESRGAN), and investigate its diagnostic value in calcified coronary plaques with the aim of suppressing blooming artifacts for the further improvement of coronary lumen assessment. We finetuned the Real-ESRGAN model and applied it to 50 patients with 184 calcified plaques detected at three main coronary arteries (left anterior descending [LAD], left circumflex [LCx] and right coronary artery [RCA]). Measurements of coronary stenosis were collected from original c

APA, Harvard, Vancouver, ISO, and other styles

3

Sun, Zhonghua, and Curtise K. C. Ng. "Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study." Diagnostics 12, no. 4 (2022): 991. http://dx.doi.org/10.3390/diagnostics12040991.

Full text

Abstract:

Background: The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence (enhanced super-resolution generative adversarial network [ESRGAN]) model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques. Methods: A total of 184 calcified plaques from 50 patients who unde

APA, Harvard, Vancouver, ISO, and other styles

4

Tan, Mengliang, Haoyang Wang, and Wentao Zhang. "SRGAN and CNN integration for enhanced chest CT diagnostics." Applied and Computational Engineering 45, no. 1 (2024): 213–19. http://dx.doi.org/10.54254/2755-2721/45/20241170.

Full text

Abstract:

Within the domain of Convolutional Neural Networks, there exists no prior application of ESRGAN for the pre-processing of CT images aimed at CNN diagonalization. The quality of CT scan images holds a direct correlation with the precision of diagnoses. Presently employed methods to enhance image quality are antiquated and have yielded less favorable outcomes. Our objective is to amalgamate ESRGAN, renowned for its prowess in enhancing image resolution, with CNN, in pursuit of a more effective and precise diagnostic procedure. In this project, we first gather datasets from the internet. And then

APA, Harvard, Vancouver, ISO, and other styles

5

Masluha, Ida, and Yufis Azhar. "Improving Classification of Medical Images Using ESRGAN-Based Upscaling and MobileNetV2." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 2 (2025): 460–70. https://doi.org/10.35882/jeeemi.v7i2.636.

Full text

Abstract:

Low-resolution photos are frequently problematic in the medical field when diagnosing skin and eye conditions since they can induce noise and lower the precision of classification algorithms. To overcome this, this research implements the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method which is used to perform upscaling, namely increasing the resolution of a low image to a high-resolution image. The research results show that ESRGAN is able to improve the quality of eye and skin images, as proven by accuracy consistency tests on the two datasets. For image classificati

APA, Harvard, Vancouver, ISO, and other styles

6

Rabbi, Jakaria, Nilanjan Ray, Matthias Schubert, Subir Chowdhury, and Dennis Chao. "Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network." Remote Sensing 12, no. 9 (2020): 1432. http://dx.doi.org/10.3390/rs12091432.

Full text

Abstract:

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a

APA, Harvard, Vancouver, ISO, and other styles

7

Rohim, Muhammad Imaduddin Abdur, Auliati Nisa, Muhammad Nurkhoiri Hindratno, et al. "Peningkatan Performa Pengenalan Wajah pada Gambar <i>Low-Resolution</i> Menggunakan Metode<i> Super-Resolution</i>." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 1 (2024): 199–208. http://dx.doi.org/10.25126/jtiik.20241117947.

Full text

Abstract:

Kartu Tanda Penduduk Elektronik (KTP-el) merupakan identitas wajib bagi penduduk Indonesia. Penyimpanan pada cip KTP-el yang mana selain digunakan untuk menyimpan gambar potret wajah individu, juga harus dapat menyimpan identitas lain seperti biodata, tanda tangan, dan sidik jari kiri dan kanan. Keterbatasan tersebut mengharuskan gambar potret wajah disimpan pada ukuran low-resolution (LR) sehingga sistem pengenalan wajah tidak optimal. Dalam penelitian ini, kami menggunakan Poznan University of Technology (PUT) Face database yang terdiri atas 200 gambar dari 100 individu. Data tersebut dilaku

APA, Harvard, Vancouver, ISO, and other styles

8

Wen, Juan, Yangjing Shi, Xiaoshi Zhou, and Yiming Xue. "Crop Disease Classification on Inadequate Low-Resolution Target Images." Sensors 20, no. 16 (2020): 4601. http://dx.doi.org/10.3390/s20164601.

Full text

Abstract:

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model trainin

APA, Harvard, Vancouver, ISO, and other styles

9

Pasupuleti, Murali Krishna. "Satellite Image Super-Resolution Using Generative Adversarial Networks (GANs)." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 539–48. https://doi.org/10.62311/nesx/rphcr15.

Full text

Abstract:

Abstract: High-resolution satellite imagery is vital for applications such as environmental monitoring, urban planning, and defense. However, the availability of such data is limited by sensor constraints and acquisition costs. This paper investigates the use of Generative Adversarial Networks (GANs) to enhance the resolution of satellite images, using low-resolution input and learning to generate high-quality details. We explore and evaluate three GAN variants—SRGAN, ESRGAN, and WDSR-GAN—on two publicly available datasets: SpaceNet and UC Merced. Quantitative results indicate that ESRGAN achi

APA, Harvard, Vancouver, ISO, and other styles

10

Helena Dewi Hapsari, Arya Dimas Wicaksana, Hafiz Fadli Faylasuf, et al. "Enhancing Low-Resolution Facial Images for Forensic Identification Using ESRGAN." International Journal of Multilingual Education and Applied Linguistics 1, no. 4 (2024): 80–92. http://dx.doi.org/10.61132/ijmeal.v1i4.156.

Full text

Abstract:

This research is motivated by the challenges in facial identification for forensic investigations due to poor image quality, especially from low-resolution CCTV recordings. Images with noise, low lighting, and suboptimal angles often hinder accurate facial recognition. This study aims to examine the effectiveness of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method in enhancing the quality of forensic facial images. The methodology consists of three main stages: data preparation of low-resolution facial images, applying the ESRGAN model to enhance image resolution, a

APA, Harvard, Vancouver, ISO, and other styles

11

Feng, Huining. "Review of GAN-Based Image Super-Resolution Techniques." Theoretical and Natural Science 52, no. 1 (2024): 146–52. http://dx.doi.org/10.54254/2753-8818/52/2024ch0134.

Full text

Abstract:

The objective of Image Super-Resolution (ISR), a significant area of study in computer vision and image processing, is to produce high-resolution images from low-resolution images. The main objective of this paper is to explore the Image Super-Resolution methods based on Generative Adversarial Networks (GANs), especially SRGAN and ESRGAN. In the experimental part, the performance of SRGAN and ESRGAN will be evaluated by using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) as evaluation metrics, and the results demonstrate the great potential of Generative Adversarial

APA, Harvard, Vancouver, ISO, and other styles

12

Li, Shuangping, Lin Gao, Bin Zhang, et al. "Research on Super-Resolution Reconstruction of Coarse Aggregate Particle Images for Earth–Rock Dam Construction Based on Real-ESRGAN." Sensors 25, no. 13 (2025): 4084. https://doi.org/10.3390/s25134084.

Full text

Abstract:

This paper investigates the super-resolution reconstruction technology of coarse granular particle images for embankment construction in earth/rock dams based on Real-ESRGAN, aiming to improve the quality of low-resolution particle images and enhance the accuracy of particle shape analysis. The paper begins with a review of traditional image super-resolution methods, introducing Generative Adversarial Networks (GAN) and Real-ESRGAN, which effectively enhance image detail recovery through perceptual loss and adversarial training. To improve the generalization ability of the super-resolution mod

APA, Harvard, Vancouver, ISO, and other styles

13

Tussupov, Jamalbek, Kairat Kozhabai, Aigulim Bayegizova, et al. "Applying machine learning to improve a texture type image." Eastern-European Journal of Enterprise Technologies 2, no. 2 (122) (2023): 13–18. http://dx.doi.org/10.15587/1729-4061.2023.275984.

Full text

Abstract:

The paper is devoted to machine learning methods that focus on texture-type image enhancements, namely the improvement of objects in images. The aim of the study is to develop algorithms for improving images and to determine the accuracy of the considered models for improving a given type of images. Although currently used digital imaging systems usually provide high-quality images, external factors or even system limitations can cause images in many areas of science to be of low quality and resolution. Therefore, threshold values for image processing in a certain field of science are consider

APA, Harvard, Vancouver, ISO, and other styles

14

Jamalbek, Tussupov, Kozhabai Kairat, Bayegizova Aigulim, et al. "Applying machine learning to improve a texture type image." Eastern-European Journal of Enterprise Technologies 2, no. 2(122) (2023): 13–18. https://doi.org/10.15587/1729-4061.2023.275984.

Full text

Abstract:

The paper is devoted to machine learning methods that focus on texture-type image enhancements, namely the improvement of objects in images. The aim of the study is to develop algorithms for improving images and to determine the accuracy of the considered models for improving a given type of images. Although currently used digital imaging systems usually provide high-quality images, external factors or even system limitations can cause images in many areas of science to be of low quality and resolution. Therefore, threshold values for image processing in a certain field of science are consider

APA, Harvard, Vancouver, ISO, and other styles

15

Kumar, Chandan, Amzad Choudhary, Gurpreet Singh, and Ms Deepti Gupta. "Enhanced Super-Resolution Using GAN." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2077–80. http://dx.doi.org/10.22214/ijraset.2022.42718.

Full text

Abstract:

Abstract: Super-resolution reconstruction is an increasingly important area in computer vision. To eliminate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results. besides the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks. However, the hallucinated details are often accompanied with unpleasant artifacts. This paper presented ESRGAN model which was also based on generative adversarial networks. To further

APA, Harvard, Vancouver, ISO, and other styles

16

Salaudeen, Habeeb, and Erbuğ Çelebi. "Pothole Detection Using Image Enhancement GAN and Object Detection Network." Electronics 11, no. 12 (2022): 1882. http://dx.doi.org/10.3390/electronics11121882.

Full text

Abstract:

Many datasets used to train artificial intelligence systems to recognize potholes, such as the challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road (PNW) datasets, do not produce satisfactory results. This is due to the fact that these datasets present complex but realistic scenarios of pothole detection tasks than popularly used datasets that achieve better results but do not effectively represents realistic pothole detection task. In remote sensing, super-resolution generative adversarial networks (GAN), such as enhanced super-resolution generative adversarial

APA, Harvard, Vancouver, ISO, and other styles

17

Liu, Jingyu, Qiong Wang, Dunbo Zhang, and Li Shen. "Super-Resolution Model Quantized in Multi-Precision." Electronics 10, no. 17 (2021): 2176. http://dx.doi.org/10.3390/electronics10172176.

Full text

Abstract:

Deep learning has achieved outstanding results in various tasks in machine learning under the background of rapid increase in equipment’s computing capacity. However, while achieving higher performance and effects, model size is larger, training and inference time longer, the memory and storage occupancy increasing, the computing efficiency shrinking, and the energy consumption augmenting. Consequently, it’s difficult to let these models run on edge devices such as micro and mobile devices. Model compression technology is gradually emerging and researched, for instance, model quantization. Qua

APA, Harvard, Vancouver, ISO, and other styles

18

Баринов, Д. М., А. И. Данилюк та Д. А. Ренсков. "Анализ метода генерации изображений высокого разрешения ESRGAN". ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 97, № 12 (2023): 29–32. http://dx.doi.org/10.18411/trnio-05-2023-650.

Full text

Abstract:

В данной статье рассмотрен метод генерации изображений высокого разрешения ESRGAN. Описаны его преимущества, недостатки, ближайшие конкуренты, а также применение данного метода в образовательной и медицинской сфере. В статье метод представлен как шаг в развитии технологий компьютерного зрения и искусственного интеллекта

APA, Harvard, Vancouver, ISO, and other styles

19

Baek, Bosung, Aro Kim, Jaeyun Jang, et al. "Efficient ESRGAN Compression Using Enhanced Knowledge Distillation Method." JOURNAL OF BROADCAST ENGINEERING 29, no. 6 (2024): 832–41. https://doi.org/10.5909/jbe.2024.29.6.832.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lim, Taeyoon, Yongjin Jo, Seokhaeng Heo, and Jaekwan Ryu. "Development of compound eye image quality improvement based on ESRGAN." Journal of the Korea Computer Graphics Society 30, no. 2 (2024): 11–19. http://dx.doi.org/10.15701/kcgs.2024.30.2.11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Park, Changhan. "Super-resolution of SAR Target Images Using Real-ESRGAN." Journal of Institute of Control, Robotics and Systems 30, no. 1 (2024): 13–19. http://dx.doi.org/10.5302/j.icros.2024.23.0170.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Khanin, D., and V. Otenko. "EVALUATION OF DEEP LEARNING-BASED SUPER-RESOLUTION METHODS FOR ENHANCED FACIAL IDENTIFICATION ACCURACY." Computer systems and network 7, no. 1 (2025): 295–306. https://doi.org/10.23939/csn2025.01.295.

Full text

Abstract:

This paper presents a comparative analysis of modern super-resolution (SR) methods for improving the accuracy of face recognition in video surveillance systems. The low quality of images obtained from surveillance cameras is a significant obstacle to effective person identification, making the use of SR methods particularly relevant. Both classical interpolation methods (bicubic interpolation) and deep learning-based methods, including convolutional neural networks (SRCNN) and generative adversarial networks (ESRGAN, Real-ESRGAN, FSRNet), are analyzed. The methods were evaluated based on crite

APA, Harvard, Vancouver, ISO, and other styles

23

Pham, Cong Thang, Thi Thu Thao Tran, Duc Anh Bao Huynh, Quoc Cuong Nguyen, and Tien Hung Nguyen. "CT image reconstruction: integrating iterative methods with Ml-EM algorithm and deep learning models." Cybernetics and Physics 13, no. 2 (2024): 130–41. http://dx.doi.org/10.35470/2226-4116-2024-13-2-130-141.

Full text

Abstract:

Computed Tomography (CT) imaging faces limitations, including low spatial resolution and noise, particularly in low-radiation-dose imaging. To address these challenges, researchers are exploring CT image reconstruction from sinogram data. Sinograms represent X-ray absorption throughout the body, and sophisticated image reconstruction methods, including machine learning algorithms and generative adversarial networks (GANs), can improve precision and resolution without increasing patient radiation exposure. This study proposes an iterative reconstruction approach that combines filters from deep

APA, Harvard, Vancouver, ISO, and other styles

24

Alwakid, Ghadah, Walaa Gouda, and Mamoona Humayun. "Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement." Healthcare 11, no. 6 (2023): 863. http://dx.doi.org/10.3390/healthcare11060863.

Full text

Abstract:

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation

APA, Harvard, Vancouver, ISO, and other styles

25

Ma, Dongmei, and Yu Li. "Image Super-Resolution Via Gradient Guidance and Gnenrative Adversarial Network." Journal of Computing and Electronic Information Management 10, no. 1 (2023): 12–16. http://dx.doi.org/10.54097/jceim.v10i1.5074.

Full text

Abstract:

In order to solve the problem that the existing image super-resolution reconstruction model generates poor visual quality and prone to structural distortion, a deep gradient guidance based on generative adversarial network is proposed. The generator introduces the gradient branch to transfer the features of the gradient image and fuses the gradient information with the image branch to prevent the image edge distorted. Referring to MSRB, ResNext and Inception, an improved multi-scale residual block is proposed and applied to the basic module of the image branch and gradient branch, which makes

APA, Harvard, Vancouver, ISO, and other styles

26

Rodrigues de Carvalho, Eric, Bruno Légora Souza da Silva Légora Souza da Silva, and Thaís Pedruzzi do Nascimento. "Super-Resolução de Imagens em Tomografia Computadorizada de Baixa Dosagem: Comparação entre Métodos de Aprendizado Profundo." Anais do Computer on the Beach 16 (May 27, 2025): 263–70. https://doi.org/10.14210/cotb.v16.p263-270.

Full text

Abstract:

AbstractThe acquisition of high-resolution medical images is essential forthe accurate diagnosis and effective treatment of many diseases.However, obtaining high-resolution images can be limited by factorssuch as device limitations and patient exposure to radiation. Tosolve this problem, this study proposes the use of super-resolutiontechniques based on deep learning to improve the resolution ofcomputerized tomography images without increasing the patient’sexposure to radiation. The LoDoPaB-CT image dataset was used.Five deep learning-based super-resolution techniques - SRCNN,ESRGAN, SwinIR, H

APA, Harvard, Vancouver, ISO, and other styles

27

Dhanush, Siriki Sai. "Drone Detection Using Gen AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 2077–82. https://doi.org/10.22214/ijraset.2025.73322.

Full text

Abstract:

The increasing prevalence of drones in various sectors has created a pressing need for efficient and accurate detection systems to ensure airspace safety and security. This paper proposes a novel drone detection framework that combines the stateof-the-art YOLOv8 object detection model with Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for improved visual clarity and detection accuracy. The system is capable of processing both images and videos, integrating a dynamic zoom functionality to focus on regions of interest for enhanced detection precision. By applying ESRGAN-base

APA, Harvard, Vancouver, ISO, and other styles

28

Tariku, Girma, Isabella Ghiglieno, Anna Simonetto, et al. "Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification." Drones 8, no. 11 (2024): 645. http://dx.doi.org/10.3390/drones8110645.

Full text

Abstract:

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that inc

APA, Harvard, Vancouver, ISO, and other styles

29

Wu, Z., and P. Ma. "ESRGAN-BASED DEM SUPER-RESOLUTION FOR ENHANCED SLOPE DEFORMATION MONITORING IN LANTAU ISLAND OF HONG KONG." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 351–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-351-2020.

Full text

Abstract:

Abstract. Monitoring, evaluating and understanding the slopes by Interferometric Synthetic Aperture Rader (InSAR) technology are critical for both human economy and natural environment. However, the resolution limitation of existing digital elevation model (DEM) in the slope areas causes the DEM phase residues and atmospheric effects promoted, which will influence the interpret accuracy of InSAR results. In this study, we propose a novel two-step ESRGAN-based DEM SR method to effectively recover high-resolution DEM from the original version. Firstly, we pretrain an ESRGAN with a large number o

APA, Harvard, Vancouver, ISO, and other styles

30

Begum, Mrs Md Jareena. "Enhancing Image Deblurring with Advanced Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5304–7. https://doi.org/10.22214/ijraset.2025.69533.

Full text

Abstract:

Abstract: This study presents a novel framework for adaptive photo restoration by integrating GFPGAN for face-focused enhancement and Real-ESRGAN for general image refinement. Users select modes tailored to image content. The model features blur-aware preprocessing, intelligent background boosting, and output evaluation through SSIM. The application is deployed using a user-friendly Gradio interface and shows consistent performance across varied visuals.

APA, Harvard, Vancouver, ISO, and other styles

31

Akhyar, Fityanul, Elvin Nur Furqon, and Chih-Yang Lin. "Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD)." Sensors 22, no. 11 (2022): 4257. http://dx.doi.org/10.3390/s22114257.

Full text

Abstract:

Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature py

APA, Harvard, Vancouver, ISO, and other styles

32

Do, Huy, Pascal Bourdon, David Helbert, Mathieu Naudin, and Remy Guillevin. "7T MRI super-resolution with Generative Adversarial Network." Electronic Imaging 2021, no. 18 (2021): 106–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.18.3dia-106.

Full text

Abstract:

The high-resolution magnetic resonance image (MRI) provides detailed anatomical information critical for clinical application diagnosis. However, high-resolution MRI typically comes at the cost of long scan time, small spatial coverage, and low signal-to-noise ratio. The benefits of the convolutional neural network (CNN) can be applied to solve the super-resolution task to recover high-resolution generic images from low-resolution inputs. Additionally, recent studies have shown the potential to use the generative advertising network (GAN) to generate high-quality super-resolution MRIs using le

APA, Harvard, Vancouver, ISO, and other styles

33

Gao, Lizhuo. "ERDBNet: Enhanced Residual Dense Block Net --A New Net to Rich ESRGAN Image Details." Journal of Physics: Conference Series 2083, no. 4 (2021): 042026. http://dx.doi.org/10.1088/1742-6596/2083/4/042026.

Full text

Abstract:

Abstract Super resolution is applied in many digital image fields. In many cases, only a set of low-resolution images can be obtained, but the image needs a higher resolution, and then SR needs to be applied. SR technology has undergone years of development. Among them, SRGAN is the key work to introduce GAN into the SR field, which can truly restore a large number of details on the basis of low-pixel pictures. ESRGAN is a further improvement on SRGAN. By removing the BN layer in SRGAN, the effect of artifacts in SRGAN is eliminated. However, there is still a problem that the restoration of in

APA, Harvard, Vancouver, ISO, and other styles

34

Karwowska, Kinga, and Damian Wierzbicki. "Modified ESRGAN with Uformer for Video Satellite Imagery Super-Resolution." Remote Sensing 16, no. 11 (2024): 1926. http://dx.doi.org/10.3390/rs16111926.

Full text

Abstract:

In recent years, a growing number of sensors that provide imagery with constantly increasing spatial resolution are being placed on the orbit. Contemporary Very-High-Resolution Satellites (VHRS) are capable of recording images with a spatial resolution of less than 0.30 m. However, until now, these scenes were acquired in a static way. The new technique of the dynamic acquisition of video satellite imagery has been available only for a few years. It has multiple applications related to remote sensing. However, in spite of the offered possibility to detect dynamic targets, its main limitation i

APA, Harvard, Vancouver, ISO, and other styles

35

AlHalawani, Sawsan, Bilel Benjdira, Adel Ammar, Anis Koubaa, and Anas M. Ali. "DiffPlate: A Diffusion Model for Super-Resolution of License Plate Images." Electronics 13, no. 13 (2024): 2670. http://dx.doi.org/10.3390/electronics13132670.

Full text

Abstract:

License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces “DiffPlate”, a novel Dif

APA, Harvard, Vancouver, ISO, and other styles

36

Alwakid, Ghadah, Walaa Gouda, and Mamoona Humayun. "Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN." Diagnostics 13, no. 14 (2023): 2375. http://dx.doi.org/10.3390/diagnostics13142375.

Full text

Abstract:

One of the primary causes of blindness in the diabetic population is diabetic retinopathy (DR). Many people could have their sight saved if only DR were detected and treated in time. Numerous Deep Learning (DL)-based methods have been presented to improve human analysis. Using a DL model with three scenarios, this research classified DR and its severity stages from fundus images using the “APTOS 2019 Blindness Detection” dataset. Following the adoption of the DL model, augmentation methods were implemented to generate a balanced dataset with consistent input parameters across all test scenario

APA, Harvard, Vancouver, ISO, and other styles

37

Mun, Jae-Hyeon, Ki-Yeon Lee, Dong-Ju Chae, Seung-Taek Lim, and Hyun-Je Song. "A Study on Status Information Extraction of Electrical Installations Tthrough Image Super-resolution based on ESRGAN." Transactions of The Korean Institute of Electrical Engineers 71, no. 10 (2022): 1497–504. http://dx.doi.org/10.5370/kiee.2022.71.10.1497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Xu, Yamei, Tianbao Guo, and Chanfei Wang. "A Remote Sensing Image Super-Resolution Reconstruction Model Combining Multiple Attention Mechanisms." Sensors 24, no. 14 (2024): 4492. http://dx.doi.org/10.3390/s24144492.

Full text

Abstract:

Remote sensing images are characterized by high complexity, significant scale variations, and abundant details, which present challenges for existing deep learning-based super-resolution reconstruction methods. These algorithms often exhibit limited convolutional receptive fields and thus struggle to establish global contextual information, which can lead to an inadequate utilization of both global and local details and limited generalization capabilities. To address these issues, this study introduces a novel multi-branch residual hybrid attention block (MBRHAB). This innovative approach is p

APA, Harvard, Vancouver, ISO, and other styles

39

Hasan, Mousumi, Nusrat Jahan Nishat, Tanjina Rahman, Mujiba Shaima, Quazi Saad ul Mosaher, and Mohd Eftay Khyrul Alam. "A Joint Framework of GFP-GAN and Real-ESRGAN for Real-World Image Restoration." International Journal of Innovative Technology and Exploring Engineering 13, no. 2 (2024): 32–42. http://dx.doi.org/10.35940/ijitee.b9792.13020124.

Full text

Abstract:

In the current era of digitalization, the restoration of old photos holds profound significance as it allows us to preserve and revive cherished memories. However, the limitations imposed by various websites offering photo restoration services prompted our research endeavor in the field of image restoration. Our motive originated from the personal desire to restore old photos, which often face constraints and restrictions on existing platforms. As individuals, we often encounter old and faded photographs that require restoration to revive the emotions and moments captured within them. The limi

APA, Harvard, Vancouver, ISO, and other styles

40

Mousumi, Hasan. "A Joint Framework of GFP-GAN and Real-ESRGAN for Real-World Image Restoration." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 2 (2024): 32–42. https://doi.org/10.35940/ijitee.B9792.13020124.

Full text

Abstract:

<strong>Abstract:</strong> In the current era of digitalization, the restoration of old photos holds profound significance as it allows us to preserve and revive cherished memories. However, the limitations imposed by various websites offering photo restoration services prompted our research endeavor in the field of image restoration. Our motive originated from the personal desire to restore old photos, which often face constraints and restrictions on existing platforms. As individuals, we often encounter old and faded photographs that require restoration to revive the emotions and moments cap

APA, Harvard, Vancouver, ISO, and other styles

41

Park, Changhan. "Similarity Comparison of Segmentation Based on Key-points in Real-ESRGAN Super-resolution Satellite SAR Images." Journal of Institute of Control, Robotics and Systems 30, no. 8 (2024): 853–62. http://dx.doi.org/10.5302/j.icros.2024.24.0133.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Kim, Yeeun, Ayoung Nam, and Dana Yang. "Discriminator of ESRGAN based on Squeeze-and-Excitation Block and Inception Module for Qualitative Image." Journal of the Korea Computer Graphics Society 30, no. 5 (2024): 21–30. http://dx.doi.org/10.15701/kcgs.2024.30.5.21.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Salgueiro Romero, Luis, Javier Marcello, and Verónica Vilaplana. "Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks." Remote Sensing 12, no. 15 (2020): 2424. http://dx.doi.org/10.3390/rs12152424.

Full text

Abstract:

Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availa

APA, Harvard, Vancouver, ISO, and other styles

44

Yang, Qinglin, Zhou Chen, Rongxin Tang, Xiaohua Deng, and Jinsong Wang. "Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images." Astrophysical Journal Supplement Series 265, no. 2 (2023): 36. http://dx.doi.org/10.3847/1538-4365/acb3b9.

Full text

Abstract:

Abstract Solar eruptions and the solar wind are sources of space weather disturbances, and extreme-ultraviolet (EUV) observations are widely used to research solar activity and space weather forecasts. Fengyun-3E is equipped with the Solar X-ray and Extreme Ultraviolet Imager, which can observe EUV imaging data. Limited by the lower resolution, however, we research super-resolution techniques to improve the data quality. Traditional image interpolation methods have limited expressive ability, while deep-learning methods can learn to reconstruct high-quality images through training on paired da

APA, Harvard, Vancouver, ISO, and other styles

45

Wang, Meng, Zhengnan Li, Haipeng Liu, Zhaoyu Chen, and Kewei Cai. "SP-IGAN: An Improved GAN Framework for Effective Utilization of Semantic Priors in Real-World Image Super-Resolution." Entropy 27, no. 4 (2025): 414. https://doi.org/10.3390/e27040414.

Full text

Abstract:

Single-image super-resolution (SISR) based on GANs has achieved significant progress. However, these methods still face challenges when reconstructing locally consistent textures due to a lack of semantic understanding of image categories. This highlights the necessity of focusing on contextual information comprehension and the acquisition of high-frequency details in model design. To address this issue, we propose the Semantic Prior-Improved GAN (SP-IGAN) framework, which incorporates additional contextual semantic information into the Real-ESRGAN model. The framework consists of two branches

APA, Harvard, Vancouver, ISO, and other styles

46

Clabaut, Étienne, Myriam Lemelin, Mickaël Germain, Yacine Bouroubi, and Tony St-Pierre. "Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery." Remote Sensing 13, no. 20 (2021): 4044. http://dx.doi.org/10.3390/rs13204044.

Full text

Abstract:

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better q

APA, Harvard, Vancouver, ISO, and other styles

47

Aldoğan, Cemre Fazilet, Koray Aksu, and Hande Demirel. "Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model." Applied Sciences 14, no. 24 (2024): 11988. https://doi.org/10.3390/app142411988.

Full text

Abstract:

Ship detection holds great value regarding port management, logistics operations, ship security, and other crucial issues concerning surveillance and safety. Recently, ship detection from optical satellite imagery has gained popularity among the research community because optical images are easily accessible with little or no cost. However, these images’ quality and quantity of feature details are bound to their spatial resolution, which often comes in medium-low spatial resolution. Accurately detecting ships requires images with richer texture and resolution. Super-resolution is used to recov

APA, Harvard, Vancouver, ISO, and other styles

48

Gouda, Walaa, Najm Us Sama, Ghada Al-Waakid, Mamoona Humayun, and Noor Zaman Jhanjhi. "Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning." Healthcare 10, no. 7 (2022): 1183. http://dx.doi.org/10.3390/healthcare10071183.

Full text

Abstract:

An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this re

APA, Harvard, Vancouver, ISO, and other styles

49

Yanamadni, Venkata Rao, J. Seetha, T. Sathish Kumar, Sathish Kumar Kannaiah, Balajee J, and Madamanchi Brahmaiah. "Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 293–302. http://dx.doi.org/10.17762/ijritcc.v11i2s.6158.

Full text

Abstract:

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used

APA, Harvard, Vancouver, ISO, and other styles

50

Salgueiro, Luis, Javier Marcello, and Verónica Vilaplana. "SEG-ESRGAN: A Multi-Task Network for Super-Resolution and Semantic Segmentation of Remote Sensing Images." Remote Sensing 14, no. 22 (2022): 5862. http://dx.doi.org/10.3390/rs14225862.

Full text

Abstract:

The production of highly accurate land cover maps is one of the primary challenges in remote sensing, which depends on the spatial resolution of the input images. Sometimes, high-resolution imagery is not available or is too expensive to cover large areas or to perform multitemporal analysis. In this context, we propose a multi-task network to take advantage of the freely available Sentinel-2 imagery to produce a super-resolution image, with a scaling factor of 5, and the corresponding high-resolution land cover map. Our proposal, named SEG-ESRGAN, consists of two branches: the super-resolutio

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!