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
Sparse representation-based volumetric super-resolution algorithm for 3D CT images of reservoir rocks
TL;DR: A framework for sparse representation-based 3D volumetric super-resolution is proposed to enhance the resolution of 3D voxel images of reservoirs scanned with CT and used the PSNR and FSIM to evaluate it qualitatively.
Book ChapterDOI
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
TL;DR: It is demonstrated how super-resolution (SR) can be utilized to maintain adequate SNR for accurate quantification of the T\(_2\) relaxation time biomarker, while simultaneously generating high-resolution images.
Book ChapterDOI
Noninvasive Electromagnetic Methods for Brain Monitoring: A Technical Review
TL;DR: This chapter will present the review of the studies on the noninvasive electromagnetic methods for brain monitoring, diagnosis and treatment, and discuss about the present scenario of the conventional brain monitoring methods with their merits and demerits.
Journal ArticleDOI
Super-resolution of PET image based on dictionary learning and random forests
Hu Zhanli,Ying Wang,Ying Wang,Xuezhu Zhang,Mengxi Zhang,Yang Yongfeng,Xin Liu,Hairong Zheng,Dong Liang +8 more
TL;DR: Wang et al. as discussed by the authors proposed an improved super-resolution (SR) method based on dictionary learning and random forests for the PET system to improve the resolution of PET images, which can minimize noise and artifacts without blurring the edges of the PET image.
Journal ArticleDOI
Leakage aperture recognition based on ensemble local mean decomposition and sparse representation for classification of natural gas pipeline
TL;DR: A new leakage aperture recognition method is proposed that presents a feature extraction algorithm based on the Ensemble Local Mean Decomposition-K-L (Kullback-Leibler) model and Sparse Representation for Classification that can achieve higher accuracy than the traditional support vector machine and Back-Propagation classification algorithms.
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.