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Journal articles on the topic 'Real-ESRGAN'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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.

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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

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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.

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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

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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.

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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.

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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

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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.

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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

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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.

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<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

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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.

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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

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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.

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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.

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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

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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.

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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.

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Ikhsal, Muhammad Fachry, Budi Arif Dermawan, and Riza Ibnu Adam. "Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan." Journal of Applied Informatics and Computing 7, no. 1 (2023): 51–56. http://dx.doi.org/10.30871/jaic.v7i1.5562.

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Kegagalan sistem deteksi kecelakaan pada kamera CCTV dapat berpengaruh pada peningkatan angka kematian di jalan raya. Penggunaan metode CNN dalam pembangunan sistem deteksi kecelakaan CCTV sudah banyak dilakukan sebelumnya. Namun, masalah umum yang sering ditemui yaitu lensa yang kotor dan zoom varifocal yang tidak fokus secara otomatis membuat kualitas citra CCTV yang dihasilkan mengalami penurunan, sehingga berpengaruh kepada performa sistem. Pada penelitian ini dilakukan pembangunan model untuk mendeteksi kecelakaan pada citra CCTV menggunakan pre-trained model MobileNetV2 yang dilakukan op

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AKHYAR, FITYANUL, LEDYA NOVAMIZANTI, and TITA RIANTIARNI. "Sistem Inspeksi Cacat pada Permukaan Kayu menggunakan Model Deteksi Obyek YOLOv5." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, no. 4 (2022): 990. http://dx.doi.org/10.26760/elkomika.v10i4.990.

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ABSTRAKPermukaan kayu mengalami berbagai serangan serangga dan jamur, sehingga dapat menyebabkan cacat seperti pembusukan pada kayu yang berpengaruh terhadap kualitas dan harga jual kayu tersebut. Pengujian secara lapangan dengan penglihatan manusia menjadi kurang efektif, karena menghasilkan penilaian yang subjektif dan memerlukan waktu yang lama. Penelitian ini mengusulkan sistem deteksi cacat pada permukaan kayu pinus dan kayu karet menggunakan Convolutional Neural Network (CNN) dengan model YOLOv5. Pengujian sistem dilakukan menggunakan beberapa model dari YOLOv5, serta dua teknik image en

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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.

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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

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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.

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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

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Liu, Dongcai, Xianhui Wen, and Youling Zhou. "Research on an improved fish recognition algorithm based on YOLOX." ITM Web of Conferences 47 (2022): 02003. http://dx.doi.org/10.1051/itmconf/20224702003.

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The key to the development of underwater resources is to detect underwater targets quickly and accurately in real time. However, due to the influence of light, the underwater image is easy to be distorted and the contrast is low and so on, which greatly affects the performance of the detection algorithm, In order to improve the detection accuracy of underwater targets, After a detailed analysis of the underwater detection target features, The attention mechanism ECA module was added to the YOLOX model, Real-ESRGAN was used to treat multiple target and fuzzy images in detection images, the accu

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Bistroń, Marta, and Zbigniew Piotrowski. "Optimization of Imaging Reconnaissance Systems Using Super-Resolution: Efficiency Analysis in Interference Conditions." Sensors 24, no. 24 (2024): 7977. https://doi.org/10.3390/s24247977.

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Image reconnaissance systems are critical in modern applications, where the ability to accurately detect and identify objects is crucial. However, distortions in real-world operational conditions, such as motion blur, noise, and compression artifacts, often degrade image quality, affecting the performance of detection systems. This study analyzed the impact of super-resolution (SR) technology, in particular, the Real-ESRGAN model, on the performance of a detection model under disturbed conditions. The methodology involved training and evaluating the Faster R-CNN detection model with original a

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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.

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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

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Buchko, Olena, and San Byn Nhuien. "Comparative Analysis of Super-Resolution Algorithms for Image Compression." NaUKMA Research Papers. Computer Science 6 (March 24, 2024): 24–29. http://dx.doi.org/10.18523/2617-3808.2023.6.24-29.

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Image compression is essential in today’s digital age when sharing and storing high-quality images is becoming increasingly important. With the growing demand for visually appealing content, there is also a growing need for efficient image compression methods that help to store images without losing visual details.The main disadvantage of traditional compression methods is that they often degrade image quality, lead to artefacts, and cause loss of texture and colour. This problem can be significant in areas where high image quality is crucial, such as medical imaging, satellite imagery, and pr

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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.

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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

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M. L. Dhore. "Unleashing Customization in GANs through Delineation guided Image Synthesis." Communications on Applied Nonlinear Analysis 32, no. 6s (2025): 134–50. https://doi.org/10.52783/cana.v32.3281.

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Interacting with AI systems through text alone can be challenging, especially when conveying complex visual concepts. This paper presents an innovative AI system that leverages a multi-GAN framework—integrating specialized Generative Adversarial Networks (GANs) such as Pix2Pix, SketchGAN, DCGAN, and ESRGAN—to interpret and generate high-fidelity visual content based on user sketches. By employing these GANs in a sequential pipeline, the system optimizes image synthesis quality through targeted stages, from sketch refinement to high-resolution enhancement. This structured approach enhances real

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N G, Shruthi, Maddi Patla Jahnav, Maithry V Pappu, Preksha Chandrakant Wali, and Sri Lakshmi A. Nair. "Quantitative Analysis of Blood Cell Components and Detection of Malarial Parasite (P.Vivax) using Faster R-CNN." Computer Science & Engineering: An International Journal 15, no. 1 (2025): 279–303. https://doi.org/10.5121/cseij.2025.15131.

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This project introduces an advanced automated system utilizing the Faster R-CNN architecture for precise detection of red blood cells (RBCs), white blood cells (WBCs), platelets, and the malarial parasite Plasmodium vivax in blood smear images. To enhance our dataset, we employ two types of Generative Adversarial Networks (GANs): one to generate new, diverse images and Real ESRGAN to improve the resolution and quality of these images, thereby increasing the robustness and performance of our system. Aimed at aiding medical professionals in diagnosing blood disorders and malaria, our system prov

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Huang, Bin, Jiaqi Lin, Jinming Liu, et al. "Separating Chinese Character from Noisy Background Using GAN." Wireless Communications and Mobile Computing 2021 (May 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/9922017.

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Separating printed or handwritten characters from a noisy background is valuable for many applications including test paper autoscoring. The complex structure of Chinese characters makes it difficult to obtain the goal because of easy loss of fine details and overall structure in reconstructed characters. This paper proposes a method for separating Chinese characters based on generative adversarial network (GAN). We used ESRGAN as the basic network structure and applied dilated convolution and a novel loss function that improve the quality of reconstructed characters. Four popular Chinese font

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Jin-li, Yang, Li Bin, Sun Zhao-xiang, Yang A-kun, Ouyang Aiguo, and Liu Yan-de. "Detection the internal quality of watermelon seeds based on terahertz imaging combined with image compressed sensing and improved-real-ESRGAN." Computers and Electronics in Agriculture 231 (April 2025): 109993. https://doi.org/10.1016/j.compag.2025.109993.

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Andreas, Derza, Shafiq Najwan, Muhammad Fajar Raihan, et al. "Optimasi Sistem Deteksi Pencurian Motor Real-Time Menggunakan YOLO dan TensorRT." RIGGS: Journal of Artificial Intelligence and Digital Business 4, no. 2 (2025): 3957–64. https://doi.org/10.31004/riggs.v4i2.1147.

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Tingginya angka pencurian sepeda motor menuntut solusi keamanan yang proaktif dan otomatis, mengingat sistem pengawasan konvensional umumnya bersifat reaktif dan kurang efektif. Penelitian ini mengusulkan sistem deteksi dini pencurian motor berbasis video yang mampu mengenali objek, mengidentifikasi individu, dan mendeteksi aktivitas mencurigakan secara real-time. Sistem ini mengintegrasikan berbagai teknologi kecerdasan buatan, termasuk YOLOv11 untuk deteksi objek, ByteTrack untuk pelacakan, InsightFace untuk identifikasi wajah, PaddleOCR untuk pembacaan pelat nomor, dan Real-ESRGAN untuk pen

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Patel, Ayush. "Chest X-ray Image Super-Resolution Using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 401–11. https://doi.org/10.22214/ijraset.2025.67257.

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Chest X-ray (CXR) imaging is essential for diagnosing respiratory diseases like pneumonia, but low-resolution (LR) images can make detection less accurate. This study explores how deep learning-based super-resolution (SR) techniques can improve CXR image quality and enhance automated pneumonia detection. Real-ESRNet utilized as the generator to restore image details and Real-ESRGAN NetD as the discriminator to refine structures. The model was fine-tuned on 4,500 images, including original and multi-scale variations, randomly selected from the Random sample of NIH Chest X-ray Dataset, which con

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Das, Ankit, Deven Prakash Paramaj, and Shambhavi BR. "Scalable Video Fidelity Enhancement: Leveraging the state-of-the-art AI Models." Scalable Computing: Practice and Experience 25, no. 3 (2024): 1658–66. http://dx.doi.org/10.12694/scpe.v25i3.2696.

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Improving visual quality is crucial as we navigate through the vast world of data. State-of-the-art (SOTA) artificial intelligence (AI) models provide highly effective solutions. Driven by the ever-growing demand for high-fidelity multimedia content, this research explores the groundbreaking capabilities of SOTA AI models to revolutionize video quality enhancement. Existing video capture methods often struggle with limitations in hardware, bandwidth, and compression, leading to subpar visual experiences. To address this challenge, we propose a novel Video Quality Enhancement Solution (VQES) th

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Jin-li, Yang, Li Bin, Yang A-kun, et al. "A generalized model for seed internal quality detection based on terahertz imaging technology combined with image compressed sensing and improved-real ESRGAN." Microchemical Journal 208 (January 2025): 112410. https://doi.org/10.1016/j.microc.2024.112410.

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Ramadani, Daffa Tama, Riza Ibnu Adam, Jajam Haerul Jaman, Chaerur Rozikin, and G. Garno. "Pengenalan Wajah Resolusi Rendah Menggunakan Arsitektur Lightweight VarGFaceNet dengan Adaptive Margin Loss." Journal of Applied Informatics and Computing 7, no. 1 (2023): 98–105. http://dx.doi.org/10.30871/jaic.v7i1.5831.

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Pengenalan wajah merupakan solusi keamanan modern yang cepat dan mudah di integrasikan pada kebanyakan device yang ada saat ini, sehingga sistem ini banyak diterapkan pada beberapa domain sebagai salah satu otorisasi keamanan. Pengembangan model pengenalan wajah menggunakan arsitektur mainstream (AlexNet, VGGNet, GoogleNet, ResNet, dan SENet) dapat menyebabkan model pengenalan wajah sulit diimplementasikan pada perangkat mobile dan embedded system. Selain itu input dengan resolusi yang rendah seperti pada footage kamera pengawas CCTV ataupun drone menyebabkan model kesulitan untuk mengenali wa

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Mufid, Tsaqif Mu'tashim, Riza Ibnu Adam, Jajam Khaeru Jaman, Garno Garno, and Iqbal Maulana. "Implementation of Identity Loss Function on Face Recognition of Low-Resolution Faces With Light CNN Architecture." Journal of Applied Informatics and Computing 8, no. 1 (2024): 91–97. http://dx.doi.org/10.30871/jaic.v8i1.6274.

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Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes th

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Huang, Yingkang, Xiaorong Wen, Yuanyun Gao, Yanli Zhang, and Guozhong Lin. "Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning." Remote Sensing 15, no. 11 (2023): 2942. http://dx.doi.org/10.3390/rs15112942.

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We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, moti

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Ru, Xin, Ran Chen, Laihu Peng, and Weimin Shi. "Fast Automatic Fuzzy C-Means Knitting Pattern Color-Separation Algorithm Based on Superpixels." Sensors 24, no. 1 (2024): 281. http://dx.doi.org/10.3390/s24010281.

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Patterns entered into knitting CAD have thousands or tens of thousands of different colors, which need to be merged by color-separation algorithms. However, for degraded patterns, the current color-separation algorithms cannot achieve the desired results, and the clustering quantity parameter needs to be managed manually. In this paper, we propose a fast and automatic FCM color-separation algorithm based on superpixels, which first uses the Real-ESRGAN blind super-resolution network to clarify the degraded patterns and obtain high-resolution images with clear boundaries. Then, it uses the impr

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Abhinav, Sivakumar, and Sridhar Ranganathan Dr. "Optimizing Affordable Drone Surveillance with Advanced Image Processing Techniques." Engineering and Technology Journal 9, no. 04 (2024): 3829–37. https://doi.org/10.5281/zenodo.11076910.

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The widespread adoption of drones can be attributed to their low cost and convenience which led to a growth in their use for surveillance reasons leading to their extensive use in other areas too. In spite of this, the problem of maximizing their production while simultaneously minimizing their expenses is still one that they face. This paper provides a comprehensive methodology that can improve the efficiency of drone surveillance at a cheaper cost. This methodology is accomplished through the application of contemporary image processing technology. The employment of RRDB ESRGAN for the purpo

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Wu, Tao, Shuo Xiong, Hui Liu, et al. "PSRGAN: Perception-Design-Oriented Image Super Resolution Generative Adversarial Network." Electronics 12, no. 21 (2023): 4420. http://dx.doi.org/10.3390/electronics12214420.

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Among recent state-of-the-art realistic image super-resolution (SR) intelligent algorithms, generative adversarial networks (GANs) have achieved impressive visual performance. However, there has been the problem of unsatisfactory perception of super-scored pictures with unpleasant artifacts. To address this issue and further improve visual quality, we proposed a perception-design-oriented PSRGAN with double perception turbos for real-world SR. The first-perception turbo in the generator network has a three-level perception structure with different convolution kernel sizes, which can extract mu

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Dakhil, Radhwan Adnan, and Ali Retha Hasoon Khayeat. "Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments." International Journal of Electrical and Electronics Research 11, no. 4 (2023): 1209–18. http://dx.doi.org/10.37391/ijeer.110443.

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This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semant

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Wang, Xinyu, Zurui Ao, Runhao Li, Yingchun Fu, Yufei Xue, and Yunxin Ge. "Super-Resolution Image Reconstruction Method between Sentinel-2 and Gaofen-2 Based on Cascaded Generative Adversarial Networks." Applied Sciences 14, no. 12 (2024): 5013. http://dx.doi.org/10.3390/app14125013.

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Due to the multi-scale and spectral features of remote sensing images compared to natural images, there are significant challenges in super-resolution reconstruction (SR) tasks. Networks trained on simulated data often exhibit poor reconstruction performance on real low-resolution (LR) images. Additionally, compared to natural images, remote sensing imagery involves fewer high-frequency components in network construction. To address the above issues, we introduce a new high–low-resolution dataset GF_Sen based on GaoFen-2 and Sentinel-2 images and propose a cascaded network CSWGAN combined with

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Karne, Sravanthi. "Realistic Video Synthesis from Audio using GAN." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 757–61. https://doi.org/10.22214/ijraset.2025.73064.

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Realistic video generation from audio input is a challenging and emerging domain in the intersection of natural language processing, computer vision, and generative modeling. The ability to automatically generate coherent and visually compelling video content from raw audio has promising applications in media creation, virtual education, assistive technologies, and entertainment. Manual video creation remains time-consuming and skill-intensive, while automated solutions often lack semantic alignment and visual realism. To address this gap, this project proposes an end-to-end intelligent pipeli

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Zhao, Yafeng, Shuai Zhang, and Junfeng Hu. "Forest Single-Frame Remote Sensing Image Super-Resolution Using GANs." Forests 14, no. 11 (2023): 2188. http://dx.doi.org/10.3390/f14112188.

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Generative Adversarial Networks (GANs) possess remarkable fitting capabilities and play a crucial role in the field of computer vision. Super-resolution restoration is the process of converting low-resolution images into high-resolution ones, providing more detail and information. This is of paramount importance for monitoring and managing forest resources, enabling the surveillance of vegetation, wildlife, and potential disruptive factors in forest ecosystems. In this study, we propose an image super-resolution model based on Generative Adversarial Networks. We incorporate Multi-Scale Residua

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Sonia Victor Soans. "Enhancing Real-Time Video Processing With Artificial Intelligence: Overcoming Resolution Loss, Motion Artifacts, And Temporal Inconsistencies." Journal of Information Systems Engineering and Management 10, no. 33s (2025): 1127–38. https://doi.org/10.52783/jisem.v10i33s.6540.

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Purpose: Traditional video processing techniques often struggle with critical challenges such as low resolution, motion artifacts, and temporal inconsistencies, especially in real-time and dynamic environments. Conventional interpolation methods for upscaling suffer from blurring and loss of detail, while motion estimation techniques frequently introduce ghosting and tearing artifacts in fast-moving scenes. Furthermore, many traditional video processing algorithms process frames independently, resulting in temporal instability, which causes flickering effects and unnatural motion transitions.

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Younis, Muhammad Waqar, Saritha, Bhavya Kallapu, et al. "Exploring the Influence of Tropical Cyclones on Regional Air Quality Using Multimodal Deep Learning Techniques." Sensors 24, no. 21 (2024): 6983. http://dx.doi.org/10.3390/s24216983.

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Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to an

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Zheng, Ao, Shouming Qi, Yanquan Cheng, Di Wu, and Jiasong Zhu. "Efficient Detection of Apparent Defects in Subway Tunnel Linings Based on Deep Learning Methods." Applied Sciences 14, no. 17 (2024): 7824. http://dx.doi.org/10.3390/app14177824.

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High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the fo

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Dong, Di, Qingxiang Shi, Pengcheng Hao, et al. "Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images." Journal of Marine Science and Engineering 12, no. 6 (2024): 1012. http://dx.doi.org/10.3390/jmse12061012.

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The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore eva

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Miao, Jiawei, Liangping Tu, Hao Liu, and Jian Zhao. "Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies." Astrophysical Journal Supplement Series 278, no. 2 (2025): 35. https://doi.org/10.3847/1538-4365/adca34.

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Abstract Galaxy–galaxy mergers are crucial in galaxy evolution, but the tidal features around galaxies are often faint, making it difficult to identify interacting or merging galaxies. High-resolution images of galaxies can identify fine structures within galaxies, which are essential for identifying and distinguishing different substructures within merging systems. However, due to observational and instrumental limitations, galaxy data is often collected at low resolution. To further improve visual quality and enhance the details of galaxy structures, we propose a dual-branch network structur

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Holenko, Maksym Yu. "Adaptive super-resolution integration to enhance object detection on low-quality unmanned aerial vehicleimagery." Herald of Advanced Information Technology 8, no. 2 (2025): 164–78. https://doi.org/10.15276/hait.08.2025.10.

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The article addresses the problem of improving the accuracy of object detection in images captured by unmanned aerial vehicles under conditions of reduced spatial resolution and the presence of noise artifacts. The relevance of this research is driven by the practical need to maintain the reliability of computer vision systems in challenging field environments, where conventional detection algorithms tend to lose effectiveness.The aim of the study is to enhance the robustness of object detection in low-quality unmanned aerial vehiclesimagery through the development of an adaptive preprocessing

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Sarode, Raj, Samiksha Varpe, Omkar Kolte, and Leena Ragha. "Image Super Resolution using Enhanced Super Resolution Generative Adversarial Network." ITM Web of Conferences 44 (2022): 03054. http://dx.doi.org/10.1051/itmconf/20224403054.

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Aside from enhancing the accuracy and speed of single picture modification utilizing fast and in-depth convolutional emotional networks, one significant challenge remains mostly commonly unaddressed, namely how do we recover soft texture details when we concentrate too much on exceptional improvement features? The resultant evaluations offer greater transmission ratings, but the high frequency data is non-existent and unsatisfactory mostly in sense that now it fails to meet the consistency anticipated in high resolution. The resulting ratings have higher signal-to-audio ratings, but the high f

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Han, Hao, Wen Du, Ziyi Feng, Zhonghui Guo, and Tongyu Xu. "An Effective Res-Progressive Growing Generative Adversarial Network-Based Cross-Platform Super-Resolution Reconstruction Method for Drone and Satellite Images." Drones 8, no. 9 (2024): 452. http://dx.doi.org/10.3390/drones8090452.

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In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep resi

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Nandal, P., Sudesh Pahal, Ashish Khanna, and Placido Rogério Pinheiro. "Super-resolution of medical images using real ESRGAN." IEEE Access, 2024, 1. http://dx.doi.org/10.1109/access.2024.3497002.

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Березкин, А. А., Х. До Фук, Р. В. Киричек, and А. А. Захаров. "ANALYSIS OF ULTRA-HIGH RESOLUTION NEURAL NETWORK MODELS IN THE UAV VIDEO STREAM COMPRESSION SYSTEM." Электросвязь, no. 3(52) (March 26, 2024). http://dx.doi.org/10.34832/elsv.2024.52.3.007.

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В статье исследуются нейронные сети суперразрешения для улучшения качества сжатого видеопотока от беспилотной системы при управлении от первого лица (FPV). Проводится сравнительный анализ современных диффузионных и генеративно-состязательных моделей: модель латентной диффузии (LDM), расширенные генеративные состязательные сети сверхрегуляции (ESRGAN) и SwinIR. Используя наборы данных FPV управления, модели настраиваются путем изменения ключевых гиперпараметров. В результате проведенных исследований выявлено, что ESRGAN обеспечивает лучшую производительность при работе в реальном времени, но пр

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Zhu, Zhengwei, Yushi Lei, Yilin Qin, Chenyang Zhu, and Yanping Zhu. "IRE: Improved Image Super-Resolution Based On Real-ESRGAN." IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3256086.

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YİĞİT, Settar. "Improving object detection of UAV images with Real-ESRGAN." Recent Advances in Science and Engineering, 2023, 33–39. http://dx.doi.org/10.14744/rase.2023.0004.

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Sreelakshmy, I. J., and Binsu C. Kovoor. "Generative Inpainting of High-resolution Images: Redefined with Real-ESRGAN." International Journal on Artificial Intelligence Tools 31, no. 05 (2022). http://dx.doi.org/10.1142/s021821302250035x.

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Embracing generative models to the inpainting realm has resulted in its overwhelming acceptability as an image editing technique. However, these generative inpainting techniques tend to be cumbersome in view of the large memory footprint and resources consumed during the operation. This accounts for why images above 2k resolution are far from being considered as input for inpainting operations. In the present era of super-resolution, where human eyes are accustomed to viewing images beyond 6k and 8k, inpainting algorithms were not at par with the resolution benchmark. This paper proposes a hig

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