Journal ArticleDOI
Sparse representation-based MRI super-resolution reconstruction
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
Pilot Lightweight Denoising Algorithm for Multiple Sclerosis on Spine MRI
Dissertation
Probabilistic modeling of tensorial data for enhancing spatial resolution in magnetic resonance imaging.
Vargas Cardona,Hernán Darío +1 more
TL;DR: A probabilistic framework based on stochastic processes (STs) for enhancing spatial resolution in different modalities of magnetic resonance imaging is proposed and is robust to high presence of noise.
Journal ArticleDOI
Mouse brain MR super-resolution using a deep learning network trained with optical imaging data
Zifei Liang,Jiangyang Zhang +1 more
TL;DR: In this article , the authors used high-resolution mouse brain auto-fluorescence (AF) data acquired using serial two-photon tomography (STPT) to examine the performance of deep learning-based super-resolution (SR) for mouse brain images.
Posted Content
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
TL;DR: In this paper, the authors demonstrate how super-resolution can be utilized to maintain adequate SNR for accurate quantification of the T2 relaxation time biomarker, while simultaneously generating high-resolution images.
An Adaptive Super-Resolution Algorithm Applied to Magnetic Resonance Imaging
TL;DR: An adaptive super-resolution algorithm is presented which can detect MRI scans defects and try to reconstruct them and can be emplyed in MRI for better results in real-time in terms of sensitivity and accuracy.
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.