research-article

MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior

Authors:

Minghui DuAuthors Info & Claims

Computers in Biology and Medicine, Volume 58, Issue C

Pages 130 - 145

https://doi.org/10.1016/j.compbiomed.2014.12.023

Published: 01 March 2015 Publication History

Abstract

In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. Do MR image SR by jointly using sparsity prior, nonlocal similarity, and sparse derivative prior.Use multi-scale first- and second-order derivative to estimate high-frequency information.Use sparse derivative prior based post-processing to suppress blurring effects in MR images.

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Cited By

Li HJia YZhu HHan BDu JLiu Y(2024)Multi-level feature extraction and reconstruction for 3D MRI image super-resolutionComputers in Biology and Medicine10.1016/j.compbiomed.2024.108151171:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108151
Sharma SVaish VGupta S(2022)An Optimized MRI Contrast Enhancement Scheme Using Cycle Generative Adversarial NetworkSN Computer Science10.1007/s42979-022-01261-33:5Online publication date: 13-Jul-2022
https://dl.acm.org/doi/10.1007/s42979-022-01261-3
Aghabiglou AEksioglu E(2021)MR image reconstruction using densely connected residual convolutional networksComputers in Biology and Medicine10.1016/j.compbiomed.2021.105010139:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.105010
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MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior
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Published In

cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 58, Issue C

March 2015

163 pages

ISSN:0010-4825

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2015

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Cited By

Li HJia YZhu HHan BDu JLiu Y(2024)Multi-level feature extraction and reconstruction for 3D MRI image super-resolutionComputers in Biology and Medicine10.1016/j.compbiomed.2024.108151171:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108151
Sharma SVaish VGupta S(2022)An Optimized MRI Contrast Enhancement Scheme Using Cycle Generative Adversarial NetworkSN Computer Science10.1007/s42979-022-01261-33:5Online publication date: 13-Jul-2022
https://dl.acm.org/doi/10.1007/s42979-022-01261-3
Aghabiglou AEksioglu E(2021)MR image reconstruction using densely connected residual convolutional networksComputers in Biology and Medicine10.1016/j.compbiomed.2021.105010139:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.105010
Li J(2018)Sparse representation based single image super-resolution with low-rank constraint and nonlocal self-similarityMultimedia Tools and Applications10.1007/s11042-017-4399-177:2(1693-1714)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s11042-017-4399-1
Natali MTagliafico GPatan G(2017)Local up-sampling and morphological analysis of low-resolution magnetic resonance imagesNeurocomputing10.1016/j.neucom.2016.10.096265:C(42-56)Online publication date: 22-Nov-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.10.096
Yang ZChai YChen TQu J(2017)Smoothed $$\ell _1$$ℓ1-regularization-based line search for sparse signal recoverySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2423-421:16(4813-4828)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s00500-016-2423-4

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