Results 21 to 30 of about 1,040 (193)

Enhancing fetal ultrasound image quality and anatomical plane recognition in low-resource settings using super-resolution models [PDF]

Scientific Reports
Super-resolution (SR) techniques present a suitable solution to increase the image resolution acquired using an ultrasound device characterized by a low image resolution. This can be particularly beneficial in low-resource imaging settings.
Hafida Boumeridja, Mohammed Ammar, Mahmood Alzubaidi, Saïd Mahmoudi, Lamya Nawal Benamer, Marco Agus, Mowafa Househ, Karim Lekadir, Mostafa El Habib Daho   +8 more
doaj   +2 more sources

ESRGAN-Enhanced YOLOv12 for Rice Leaf Disease Detection With Dataset Partitioning Analysis

Journal of Information Systems and Informatics
Rice leaf diseases pose a significant threat to agricultural productivity, yet accurate automated detection remains challenging due to low image quality in field conditions.
Ahmad Fathir, Ida Mulyadi, Fahrim Irhamna   +2 more
exaly   +3 more sources

Modified ESRGAN with Uformer for Video Satellite Imagery Super-Resolution

Remote Sensing
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 ...
Kinga Karwowska, Damian Wierzbicki
doaj   +3 more sources

Enhanced retrospective forecasting in dissipative dynamical systems using transformer and multi-scale ESRGAN models

Results in Engineering
Accurate characterization of complex dynamical systems is crucial for understanding their intrinsic behavior, and retrospective prediction provides a promising solution.
Meng Zhang, Mustafa Z. Yousif, Linqi Yu, Hee-Chang Lim   +3 more
doaj   +2 more sources

Research on an improved fish recognition algorithm based on YOLOX [PDF]

ITM Web of Conferences, 2022
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 ...
Liu Dongcai, Wen Xianhui, Zhou Youling
doaj   +1 more source

'In silico labelling': Exploring the biological landscape at the nanoscale using intelligent multimodal optical microscopy. [PDF]

J Microsc
Abstract Multimodal optical microscopy has recently converged with artificial intelligence (AI) to enable in silico labelling—the computational prediction of fluorescence‐like molecular contrast from label‐free measurements. Fluorescence plays a crucial role in linking microscopy and spectroscopy at molecular level, enabling image formation by means of
Diaspro A, Bianchini P, Bizzarri R, Zanacchi FC, Civita S, Incardona N, Lanzanò L, Morgantini A, Salerno M, Touijer L.   +9 more
europepmc   +2 more sources

Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods

Journal of Water and Climate Change, 2022
In this paper, we perform the super-resolution of sea surface temperature data with the enhanced super-resolution generative adversarial network (ESRGAN), which is a deep neural network-based single-image super-resolution (SISR) method that uses a ...
Tomoki Izumi, Motoki Amagasaki, Kei Ishida, Masato Kiyama   +3 more
doaj   +1 more source

Super-resolution reconstruction of rock CT images based on Real-ESRGAN

Gong-kuang zidonghua, 2023
Due to factors such as image acquisition equipment and geological environment, rock CT images have low resolution and unclear details. However, existing image super-resolution reconstruction methods are prone to losing details when characterizing high ...
LI Gang, ZHANG Yabing, YANG Qinghe, ZOU Junpeng, CAI Tian, LIU Hang, ZHAO Yiming   +6 more
doaj   +1 more source

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [PDF]

CoRR, 2018
To appear in ECCV 2018 workshop. Won Region 3 in the PIRM2018-SR Challenge.
Xintao Wang 0002, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu 0001, Chao Dong 0005, Chen Change Loy, Yu Qiao 0001, Xiaoou Tang   +8 more
openaire   +4 more sources

Improved Pothole Detection Using YOLOv7 and ESRGAN

CoRR, 2023
Potholes are common road hazards that is causing damage to vehicles and posing a safety risk to drivers. The introduction of Convolutional Neural Networks (CNNs) is widely used in the industry for object detection based on Deep Learning methods and has achieved significant progress in hardware improvement and software implementations.
Nirmal Kumar Rout, Gyanateet Dutta, Varun Sinha, Arghadeep Dey, Subhrangshu Mukherjee, Gopal Gupta 0003   +5 more
openaire   +3 more sources