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
Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning.
TL;DR: This work hypothesizes that the estimation of a well-representative and centered CBT would help better capture the individuality of each LR brain graph via its residual distance from the population-based CBT, which will eventually allow an accurate identification of the most similar individual graphs to a new testing graph in the LR domain for the target prediction task.
Journal ArticleDOI
Projection Error Propagation-based Regularization (PEPR) method for resistivity reconstruction in Electrical Impedance Tomography (EIT)
TL;DR: In this article, a Projection Error Propagation-based Regularization (PEPR) method is proposed to improve the image quality in electrical impedance tomography (EIT), which defines the regularization parameter as a function of the projection error developed by difference between experimental measurements and calculated data.
Journal ArticleDOI
Gradient-Guided Convolutional Neural Network for MRI Image Super-Resolution
Xiaofeng Du,Yifan He +1 more
TL;DR: A gradient-guided convolutional neural network is developed for improving the reconstruction accuracy of high-frequency image details from the LR image and achieves better performance over the published state-of-art approaches.
Journal ArticleDOI
On the measurement uncertainties of THz imaging systems based on compressive sampling
Leopoldo Angrisani,Francesco Bonavolonta,Giovanni Cavallo,Annalisa Liccardo,Rosario Schiano Lo Moriello +4 more
TL;DR: Misalignment of the CS masks turns out to be the most impacting uncertainty source, as confirmed by experimental tests carried out through an actual THz Imaging system, and the performance factor estimated on the reconstructed image of a reference target is capable of highlighting the presence of an incorrect configured Imaging system.
Journal ArticleDOI
Fast single image super-resolution using estimated low-frequency k-space data in MRI
TL;DR: This study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.
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.