Wide weighted attention multi-scale network for accurate MR image super-resolution
High-quality magnetic resonance (MR) images afford more detailed information for reliable
diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep …
diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep …
MR image super-resolution with squeeze and excitation reasoning attention network
High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed
information for reliable diagnosis and quantitative image analyses. Deep convolutional …
information for reliable diagnosis and quantitative image analyses. Deep convolutional …
Pyramid orthogonal attention network based on dual self-similarity for accurate mr image super-resolution
For magnetic resonance (MR) images sharing visual characteristics, the internal structure
repetitions of different scales are considerable image-specific priors. Following the …
repetitions of different scales are considerable image-specific priors. Following the …
3d cross-scale feature transformer network for brain mr image super-resolution
W Zhang, L Wang, W Chen, Y Jia… - ICASSP 2022-2022 …, 2022 - ieeexplore.ieee.org
High-resolution (HR) magnetic resonance (MR) images could provide reliable visual
information for clinical diagnosis. Recently, super-resolution (SR) methods based on …
information for clinical diagnosis. Recently, super-resolution (SR) methods based on …
MR image super-resolution via wide residual networks with fixed skip connection
Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …
Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical
information important for clinical application and quantitative image analysis. However, HR …
information important for clinical application and quantitative image analysis. However, HR …
Residual dense network for medical magnetic resonance images super-resolution
Background and objective High-resolution magnetic resonance images (MRI) help experts
to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI …
to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI …
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 …
A hybrid convolutional neural network for super‐resolution reconstruction of MR images
Y Zheng, B Zhen, A Chen, F Qi, X Hao, B Qiu - Medical physics, 2020 - Wiley Online Library
Purpose Spatial resolution is an important parameter for magnetic resonance imaging (MRI).
High‐resolution MR images provide detailed information and benefit subsequent image …
High‐resolution MR images provide detailed information and benefit subsequent image …
Compressed multi-scale feature fusion network for single image super-resolution
Recently, deep neural networks have made significant breakthroughs in the image super-
resolution (SR) field. Most deep learning-based image SR methods learn an end-to-end …
resolution (SR) field. Most deep learning-based image SR methods learn an end-to-end …