Results 41 to 50 of about 59,582 (263)

Single Image Super Resolution Using Deep Residual Learning

AI
Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques.
Moiz Hassan, Kandasamy Illanko, Xavier N. Fernando   +2 more
doaj   +1 more source

Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks

Remote Sensing, 2020
Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data ...
Francesco Salvetti, Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge   +3 more
doaj   +1 more source

Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder

Informatics in Medicine Unlocked, 2021
Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution
J. Andrew, T.S.R. Mhatesh, Robin D. Sebastin, K. Martin Sagayam, Jennifer Eunice, Marc Pomplun, Hien Dang   +6 more
doaj   +1 more source

Neighborhood Issue in Single-Frame Image Super-Resolution [PDF]

2005 IEEE International Conference on Multimedia and Expo, 2005
Super-resolution is the problem of generating one or a set of high-resolution images from one or a sequence of low-resolution frames. Most methods have been proposed for super-resolution based on multiple low resolution images of the same scene, which is called multiple-frame super-resolution.
Xu (Kevin) Su, Qi Tian 0001, Qing Xue, Nicu Sebe, Jingsheng Ma   +4 more
openaire   +3 more sources

SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution

IEEE Access, 2022
Recent research on image and video processing using convolutional neural networks has shown remarkable improvements, especially in the area of single image super-resolution(SISR). The primary target of SISR is to recover the visually appealing high-resolution (HR) output image from the original degraded low-resolution (LR) input image.
Wazir Muhammad, Supavadee Aramvith, Takao Onoye   +2 more
openaire   +2 more sources

Quantitative Assessment of Single-Image Super-Resolution in Myocardial Scar Imaging

IEEE Journal of Translational Engineering in Health and Medicine, 2014
Single-image super resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super resolution has been demonstrated to improve image quality in scaled down images in the image ...
Hiroshi Ashikaga, Heidi L. Estner, Daniel A. Herzka, Elliot R. Mcveigh, Henry R. Halperin   +4 more
doaj   +1 more source

SREFBN: Enhanced feature block network for single‐image super‐resolution

IET Image Processing, 2022
Deep learning has assisted the field of single‐image super‐resolution (SR) in achieving new heights. However, the task of restoring a high‐resolution (HR) image from a highly degraded low‐resolution (LR) image is sophisticated due to poor image ...
Vachiraporn Ketsoi, Muhammad Raza, Haopeng Chen, Xubo Yang   +3 more
doaj   +1 more source

A Very Deep Spatial Transformer Towards Robust Single Image Super-Resolution

IEEE Access, 2019
In general, existing research on single image super-resolution does not consider the practical application that, when image transmission is over noisy channels, the effect of any possible geometric transformations could incur significant quality loss and
Jianmin Jiang, Hossam M. Kasem, Kwok-Wai Hung   +2 more
doaj   +1 more source

Infant Embryonal CNS Tumors: Molecular Insights and Treatment Considerations for Contemporary Pediatric Neuro‐Oncology

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Embryonal tumors comprise the majority of malignant central nervous system (CNS) neoplasms diagnosed in children under 3 years of age. Compared with their counterparts in older children, these tumors exhibit distinct molecular biology and a more aggressive clinical phenotype, while their management is complicated by the heightened ...
Sudarshawn Damodharan, Mariah Wright‐Nadkarni, Mohamed S. Abdelbaki, Girish Dhall   +3 more
wiley   +1 more source

Multi‐feature fusion attention network for single image super‐resolution

IET Image Processing, 2023
Single Image Super‐Resolution algorithms have made enormous progress in recent years. However, many previous Convolution Neural Network (CNN) based Super‐Resolution algorithms only stack uniform convolution layers of fixed kernel size, and frequently ...
Jiacheng Chen, Wanliang Wang, Fangsen Xing, Hangyao Tu   +3 more
doaj   +1 more source