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
Image super-resolution
TL;DR: This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.
Journal ArticleDOI
Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.
TL;DR: The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.
Journal ArticleDOI
Super-resolution musculoskeletal MRI using deep learning.
Akshay S. Chaudhari,Zhongnan Fang,Feliks Kogan,Jeffrey P. Wood,Kathryn J. Stevens,Eric K. Gibbons,Jin Hyung Lee,Garry E. Gold,Brian A. Hargreaves +8 more
TL;DR: To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods.
Journal ArticleDOI
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
TL;DR: A graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions and outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
Journal ArticleDOI
Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.
TL;DR: Experimental results show that the proposed deep convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity.
References
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Proceedings ArticleDOI
Method of optimal directions for frame design
TL;DR: Experiments demonstrate that the approximation capabilities, in terms of mean squared error (MSE), of the optimized frames are significantly better than those obtained using frames designed by the algorithm of Engan et.
Journal ArticleDOI
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
TL;DR: Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.
Journal ArticleDOI
Generalized principal component analysis (GPCA)
TL;DR: An algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points and applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views are presented.
Journal ArticleDOI
Multifocus Image Fusion and Restoration With Sparse Representation
TL;DR: A sparse representation-based multifocus image fusion method that can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm is proposed.
Journal ArticleDOI
Sparse Representation for Target Detection in Hyperspectral Imagery
TL;DR: This paper proposes a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI) based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples.