Image super-resolution: A comprehensive review, recent trends, challenges and applications
Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …
processing to improve the visual perception of the poor-quality images. The key objective of …
Super‐resolution in magnetic resonance imaging: a review
For the last 15 years, super‐resolution (SR) algorithms have successfully been applied to
magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after …
magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after …
SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning
High resolution magnetic resonance (MR) images are desired in many clinical and research
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
Super-resolution of brain MRI images based on denoising diffusion probabilistic model
Z Wu, X Chen, S Xie, J Shen, Y Zeng - Biomedical Signal Processing and …, 2023 - Elsevier
Super-resolution of brain magnetic resonance imaging (MRI) generates high resolution
brain images as opposed to low-resolution ones, thus providing more detailed anatomical …
brain images as opposed to low-resolution ones, thus providing more detailed anatomical …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
Super-resolution in medical imaging
H Greenspan - The computer journal, 2009 - ieeexplore.ieee.org
This paper provides an overview on super-resolution (SR) research in medical imaging
applications. Many imaging modalities exist. Some provide anatomical information and …
applications. Many imaging modalities exist. Some provide anatomical information and …
Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping
Neuroimaging advances have given rise to major progress in neurosciences and neurology,
as ever more subtle and specific imaging methods reveal new aspects of the brain. One …
as ever more subtle and specific imaging methods reveal new aspects of the brain. One …
Deep learning single-frame and multiframe super-resolution for cardiac MRI
Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-
matrix images reduces spatial detail. Deep learning (DL) might enable both faster …
matrix images reduces spatial detail. Deep learning (DL) might enable both faster …
Super‐resolution methods in MRI: can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time?
Improving the resolution in magnetic resonance imaging comes at the cost of either lower
signal‐to‐noise ratio, longer acquisition time or both. This study investigates whether so …
signal‐to‐noise ratio, longer acquisition time or both. This study investigates whether so …
Super-resolution of magnetic resonance images using Generative Adversarial Networks
Abstract Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …