Journal ArticleDOI
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
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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Proceedings ArticleDOI
Seismic reflectivity inversion using spectral compressed sensing
Dehui Kong,Zhenming Peng +1 more
TL;DR: Experiments results demonstrate that using the proposed method based on SCS can achieve comparable or better performance with traditional least square method.
Book ChapterDOI
Improving Image Quality and Convergence Rate of Perona–Malik Diffusion Based Compressed Sensing MR Image Reconstruction by Gradient Correction
Ajin Joy,Joseph Suresh Paul +1 more
TL;DR: A memory-based reconstruction algorithm is developed for optimizing image quality and convergence rate of Compressed Sensing-Magnetic Resonance image reconstruction using Perona–Malik diffusion by correcting the estimate of the underlying structure of the image using a combination of gradient information from a number of past iterations.
Journal ArticleDOI
PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
TL;DR: Hao et al. as mentioned in this paper proposed pixel-level non-local iterative thinning model based on compressed sensing theory, which can ensure the removal of artifacts and better restore the details in the image.
Journal ArticleDOI
An Efficient Lightweight Generative Adversarial Network for Compressed Sensing Magnetic Resonance Imaging Reconstruction
TL;DR: Wang et al. as discussed by the authors designed an efficient lightweight generative adversarial network (GAN) to achieve more accurate MRI reconstruction, which utilizes depthwise separable convolution as the basic component to reduce the number of learning parameters.
Posted Content
Multi-modal Aggregation Network for Fast MR Imaging
TL;DR: In this article, a multi-modal aggregation network (MANet) is proposed to discover complementary representations from a fully sampled auxiliary modality, with which to hierarchically guide the reconstruction of a given target modality.
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.