Y
Yunsong Liu
Researcher at University of Southern California
Publications - 16
Citations - 662
Yunsong Liu is an academic researcher from University of Southern California. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. The author has an hindex of 8, co-authored 16 publications receiving 487 citations. Previous affiliations of Yunsong Liu include Xiamen University.
Papers
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Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction
TL;DR: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality and the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction.
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Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
TL;DR: 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.
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Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
TL;DR: Wang et al. as discussed by the authors proposed a projected iterative soft thresholding algorithm (pISTA) and its acceleration pFISTA for CS-MRI image reconstruction, which exploit sparsity of the magnetic resonance (MR) images under the redundant representation of tight frames.
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Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.
TL;DR: This paper studies the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and proposes a new efficient numerical algorithm to solve the optimization problem.
Posted Content
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
TL;DR: In this paper, a fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to provide adaptive sparse representation of images, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class.