J
Jian Cheng
Researcher at Beihang University
Publications - 66
Citations - 1229
Jian Cheng is an academic researcher from Beihang University. The author has contributed to research in topics: Computer science & Diffusion MRI. The author has an hindex of 16, co-authored 58 publications receiving 984 citations. Previous affiliations of Jian Cheng include French Institute for Research in Computer Science and Automation & Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations
TL;DR: Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed image SR method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolations, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.
Proceedings ArticleDOI
Visual tracking via incremental Log-Euclidean Riemannian subspace learning
TL;DR: An effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log- eigenspace representation through adaptively updating the sample mean and eigenbasis is presented.
Book ChapterDOI
Model-free and analytical EAP reconstruction via spherical polar Fourier diffusion MRI
TL;DR: Spherical Polar Fourier Imaging (SPFI) is proposed, a novel model-free fast robust analytical EAP reconstruction method, which almost does not need any assumption of data and does not needs too many samplings.
Journal ArticleDOI
Multi‐atlas based representations for Alzheimer's disease diagnosis
TL;DR: The proposed multi‐atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases, can provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors.
Book ChapterDOI
A Riemannian Framework for Orientation Distribution Function Computing
TL;DR: A state of the art Riemannian framework for ODF computing based on Information Geometry and sparse representation of orthonormal bases and a novel scalar measurement, named Geometric Anisotropy (GA), which is the RiemANNian geodesic distance between the ODF and the isotropic ODF.