Undersampled MR image reconstruction with data-driven tight frame

DOI10.1155/2015/424087zbMath1343.92265OpenAlexW1497452282WikidataQ30982031 ScholiaQ30982031MaRDI QIDQ304681

Jianbo Liu, Dong Liang, Xi Peng, Shan-shan Wang

Publication date: 26 August 2016

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2015/424087

zbMATH Keywords

Bregman iteration algorithmdata-driven tight frame magnetic image reconstructionmagnetic resonance image reconstruction

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55)

Uses Software

• DLMRI-Lab

Cites Work

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• Ridgelets: estimating with ridge functions
• Tight frame: an efficient way for high-resolution image reconstruction
• Framelets: MRA-based constructions of wavelet frames
• Data-driven tight frame construction and image denoising
• A framelet-based image inpainting algorithm
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Split Bregman Methods and Frame Based Image Restoration
• New tight frames of curvelets and optimal representations of objects with piecewise C² singularities
• Higher Degree Total Variation (HDTV) Regularization for Image Recovery
• Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
• An Iterative Regularization Method for Total Variation-Based Image Restoration
• Compressed sensing

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