Journal ArticleDOI
Sparse representation-based MRI super-resolution reconstruction
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TLDR
A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries.About:
This article is published in Measurement.The article was published on 2014-01-01. It has received 73 citations till now. The article focuses on the topics: Real-time MRI & Sparse approximation.read more
Citations
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Journal ArticleDOI
Super-resolution reconstruction of MR image with a novel residual learning network algorithm.
TL;DR: This work proposes a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL), which works effectively in capturing high-frequency details by learning local residuals.
Journal ArticleDOI
SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning
TL;DR: An approach SMORE1 based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images is presented and is shown to be visually and quantitatively superior to previously reported methods.
Journal ArticleDOI
Channel Splitting Network for Single MR Image Super-Resolution
TL;DR: The extensive experiments on various MR images, including proton density (PD), T1, and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.
Book ChapterDOI
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end task transformer network for joint MRI reconstruction and super-resolution, which allows representations and feature transmission to be shared between multiple tasks to achieve higher-quality, super-resolved and motion-artifacts-free images from highly undersampled and degenerated MRI data.
Journal ArticleDOI
Applications of a deep learning method for anti-aliasing and super-resolution in MRI.
Can Zhao,Muhan Shao,Aaron Carass,Hao Li,Blake E. Dewey,Lotta Maria Ellingsen,Jonghye Woo,Michael A. Guttman,Ari M. Blitz,Maureen Stone,Peter A. Calabresi,Henry R. Halperin,Jerry L. Prince +12 more
TL;DR: The SMORE algorithm is reviewed and its performance in four applications is demonstrated, showing its potential for use in both research and clinical scenarios and improving the visualization of brain white matter lesions in FLAIR images acquired from multiple sclerosis patients.
References
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Journal ArticleDOI
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Journal ArticleDOI
Image Super-Resolution Via Sparse Representation
TL;DR: This paper presents a new approach to single-image superresolution, based upon sparse signal representation, which generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods.
Book ChapterDOI
On single image scale-up using sparse-representations
TL;DR: This paper deals with the single image scale-up problem using sparse-representation modeling, and assumes a local Sparse-Land model on image patches, serving as regularization, to recover an original image from its blurred and down-scaled noisy version.
Proceedings ArticleDOI
Super-resolution through neighbor embedding
TL;DR: This paper proposes a novel method for solving single-image super-resolution problems, given a low-resolution image as input, and recovers its high-resolution counterpart using a set of training examples, inspired by recent manifold teaming methods.
Journal ArticleDOI
Dictionaries for Sparse Representation Modeling
TL;DR: This paper surveys the various options such training has to offer, up to the most recent contributions and structures of the MOD, the K-SVD, the Generalized PCA and others.