Journal ArticleDOI
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
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TLDR
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.About:
This article is published in Medical Image Analysis.The article was published on 2016-01-01. It has received 150 citations till now. The article focuses on the topics: Iterative reconstruction & Wavelet transform.read more
Citations
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Journal ArticleDOI
MRI Reconstruction From Sparse K-Space Data Using Low Dimensional Manifold Model
TL;DR: The patches’ similarity and redundancy are exploited by applying the low-dimensional manifold model (LDMM) to enable reconstruction from sparse k-space measurements, and the proposed algorithm significantly increases the quality of the reconstructed images.
Journal ArticleDOI
An FPGA implementation of the matching pursuit algorithm for a compressed sensing enabled e-Health monitoring platform
Oussama Kerdjidj,Abbes Amira,Khalida Ghanem,Naeem Ramzan,Stamos Katsigiannis,Fatima Chouireb +5 more
TL;DR: Comparisons against other state-of-the-art methods showed that the low complexity of the MP algorithm can be exploited for providing almost similar results to more complex algorithms using 87–583 less Digital Signal Processor cores, 28–540 less Block RAMs and 10,300 to 84,700 less Look-Up Table (LUT) slices.
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Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme
TL;DR: A novel fast interpolated compressed sensing technique based on 2D variable density under-sampling (VRDU) scheme that has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content.
Posted Content
Image Reconstruction with Low-rankness and Self-consistency of k-space Data in Parallel MRI
TL;DR: Wang et al. as mentioned in this paper explored the simultaneous two-directional low-rankness (STDLR) in the k-space data and mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT).
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Compressed Sensing MRI Using Sparsity Averaging and FISTA
TL;DR: This paper presents to use sparsity averaging prior for CS-MRI reconstruction in the basis of that MR images have average sparsity over multiple wavelet frames, using a Fast Iterative Shrinkage Thresholding Algorithm (FISTA).
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
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
An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint
TL;DR: It is proved that replacing the usual quadratic regularizing penalties by weighted 𝓁p‐penalized penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem.