Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint

2016 ◽  
Vol 77 (3) ◽  
pp. 1359-1366 ◽  
Author(s):  
Congyu Liao ◽  
Ying Chen ◽  
Xiaozhi Cao ◽  
Song Chen ◽  
Hongjian He ◽  
...  
Keyword(s):  
High Resolution ◽  
Low Rank ◽  
Diffusion Data ◽  
Rank Constraint ◽  
Parallel Reconstruction

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

scholarly journals Accelerating parameter mapping with a locally low rank constraint

2014 ◽  
Vol 73 (2) ◽  
pp. 655-661 ◽  
Author(s):  
Tao Zhang ◽  
John M. Pauly ◽  
Ives R. Levesque
Keyword(s):  
Low Rank ◽  
Parameter Mapping ◽  
Rank Constraint

scholarly journals High-Resolution Cardiovascular MRI by Integrating Parallel Imaging With Low-Rank and Sparse Modeling

2013 ◽  
Vol 60 (11) ◽  
pp. 3083-3092 ◽  
Author(s):  
Anthony G. Christodoulou ◽  
Haosen Zhang ◽  
Bo Zhao ◽  
T. Kevin Hitchens ◽  
Chien Ho ◽  
...  
Keyword(s):  
High Resolution ◽  
Parallel Imaging ◽  
Low Rank ◽  
Cardiovascular Mri ◽  
Sparse Modeling

MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

2016 ◽  
Vol 43 (6Part30) ◽  
pp. 3701-3701 ◽  
Author(s):  
Q Xu ◽  
H Liu ◽  
H Yu ◽  
G Wang ◽  
L Xing
Keyword(s):  
Dictionary Learning ◽  
Low Dose ◽  
Low Rank ◽  
Spectral Ct ◽  
Rank Constraint

scholarly journals Superpixel Spectral Unmixing for Hyperspectral Image Superresolution Using a Coupled Encoder-Decoder Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shao-lei Zhang ◽  
Guang-yuan Fu ◽  
Hong-qiao Wang ◽  
Yu-qing Zhao
Keyword(s):  
Hyperspectral Image ◽  
Visual Quality ◽  
Spectral Unmixing ◽  
The Other ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Spectral Information ◽  
Multispectral Images ◽  
Self Similarity ◽  
Rank Constraint

In this paper, we propose a novel hyperspectral image superresolution method based on superpixel spectral unmixing using a coupled encoder-decoder network. The hyperspectral image and multispectral images are fused to generate high-resolution hyperspectral images through the spectral unmixing framework with low-rank constraint. Specifically, the endmember and abundance information is extracted via a coupled encoder-decoder network integrating the priori for unmixing. The coupled network consists of two encoders and one shared decoder, where spectral information is preserved through the encoder. The multispectral image is clustered into superpixels to explore self-similarity, and then, the superpixels are unmixed to obtain an abundance matrix. By imposing a low-rank constraint on the abundance matrix, we further improve the superresolution performance. Experiments on the CAVE and Harvard datasets indicate that our superresolution method outperforms the other compared methods in terms of quantitative evaluation and visual quality.

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