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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Journal ArticleDOI
Compressive Sensing Magnetic Resonance Image Reconstruction and Denoising using Convolutional Neural Network
R. Singh,Lakhwinder Kaur +1 more
TL;DR: An improved CNN-based MRI reconstructed algorithm has been presented in this paper which shows better performance to reconstruct high-quality MRI than similar other MR image reconstruction algorithms.
Posted ContentDOI
MRI Reconstruction with Enhanced Self-Similarity Using Graph Convolutional Network
TL;DR: Zhang et al. as discussed by the authors incorporated a graph to represent non-local information, and improved the reconstructed images by Enhanced Self-Similarity Using Graph Convolutional Network (GCESS).
Proceedings ArticleDOI
A Rapid Non-Linear Diffusion Compressed Sensing parallel MR Image Reconstruction
Ajin Joy,Joseph Suresh Paul +1 more
TL;DR: This paper proposes an â-switching NLDR technique for a faster approximation of reconstruction image without affecting the image quality and exploits the difference in the extent of undersampling artifacts in signal-background regions of the channel images to arrive at different estimates of contrast parameter, leading to an effective optimization of speed and quality.
Posted Content
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction
TL;DR: In this paper, a pre-trained generative adversarial network (GAN) and transfer learning was used to improve the reconstruction performance of a pretrained GAN model with parallel imaging.
Journal ArticleDOI
WRANet: wavelet integrated residual attention U-Net network for medical image segmentation
TL;DR: In this paper , a wavelet residual attention network (WRANet) is proposed to improve the robustness and segmentation performance of the network, which replaces the standard downsampling modules (e.g., maximum pooling and average pooling) in CNNs with discrete wavelet transform, decompose the features into low-and high-frequency components, and remove the highfrequency components to eliminate noise.
References
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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.
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$rm K$ -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.