scholarly journals Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 509 ◽  
Author(s):  
Ryan Liu ◽  
Lin Shi ◽  
Simon Yu ◽  
Naixue Xiong ◽  
Defeng Wang
Keyword(s):  
Dynamic Mri ◽  
Low Rank ◽  
Sparsity Constraints

Non-convex algorithm for sparse and low-rank recovery: Application to dynamic MRI reconstruction

2013 ◽  
Vol 31 (3) ◽  
pp. 448-455 ◽  
Author(s):  
Angshul Majumdar ◽  
Rabab K. Ward ◽  
Tyseer Aboulnasr
Keyword(s):  
Dynamic Mri ◽  
Low Rank ◽  
Mri Reconstruction

scholarly journals Exploring Inter-Instance Relationships within the Query Set for Robust Image Set Matching

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5051 ◽  
Author(s):  
Deyin Liu ◽  
Chengwu Liang ◽  
Zhiming Zhang ◽  
Lin Qi ◽  
Brian C. Lovell
Keyword(s):  
Optimization Problems ◽  
Low Rank ◽  
Collaborative Representation ◽  
Alternating Direction ◽  
Proposed Model ◽  
Class Level ◽  
Sparsity Constraints ◽  
Image Set ◽  
Robust Image ◽  
Public Datasets

Image set matching (ISM) has attracted increasing attention in the field of computer vision and pattern recognition. Some studies attempt to model query and gallery sets under a joint or collaborative representation framework, achieving impressive performance. However, existing models consider only the competition and collaboration among gallery sets, neglecting the inter-instance relationships within the query set which are also regarded as one important clue for ISM. In this paper, inter-instance relationships within the query set are explored for robust image set matching. Specifically, we propose to represent the query set instances jointly via a combined dictionary learned from the gallery sets. To explore the commonality and variations within the query set simultaneously to benefit the matching, both low rank and class-level sparsity constraints are imposed on the representation coefficients. Then, to deal with nonlinear data in real scenarios, the`kernelized version is also proposed. Moreover, to tackle the gross corruptions mixed in the query set, the proposed model is extended for robust ISM. The optimization problems are solved efficiently by employing singular value thresholding and block soft thresholding operators in an alternating direction manner. Experiments on five public datasets demonstrate the effectiveness of the proposed method, comparing favorably with state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document