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
4D computed tomography super-resolution reconstruction based on tensor product and nuclear norm optimization
TL;DR: In this article , a tensor product and nuclear norm optimization method was proposed to improve the resolution of the 4D-CT image, which can extract useful information from each dimension of LR image tensors to enhance the equality of the reconstruction.
Posted Content
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution.
TL;DR: In this article, a separable attention network is proposed to explore foreground and background areas in the forward and reverse directions with the help of the auxiliary contrast, enabling it to learn clearer anatomical structures and edge information for the target-contrast MR image.
Proceedings ArticleDOI
Single-image super-resolution based on sparse kernel ridge regression
Fanlu Wu,Xiangjun Wang +1 more
TL;DR: An example-based algorithm is proposed to implement Super-resolution (SR) reconstruction by single-image, finding the optimal sparse subset of the training data set by kernel matching pursuit (KMP).
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Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end task transformer network (T$^2$Net) for joint MRI reconstruction and super-resolution, which allows representations and feature transmission to be shared between multiple tasks to achieve higher-quality, super-resolved and motion-artifacts-free images from highly undersampled and degenerated MRI data.
Journal ArticleDOI
Assessing the Utility of Low Resolution Brain Imaging: Treatment of Infant Hydrocephalus
Joshua Harper,Venkateswararao Cherukuri,Tom O’Reilly,Mingzhao Yu,Edith Mbabazi-Kabachelor,Ronald Mulando,Kevin N. Sheth,Andrew G. Webb,Benjamin C. Warf,Abhaya V. Kulkarni,Vishal Monga,Steven J. Schiff +11 more
TL;DR: In this article, a post-hoc analysis of an ongoing randomized clinical trial, assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning.
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.