Paper Info
Title
An Algorithm for Parallel Reconstruction of Jointly Sparse Tensors with Applications to Hyperspectral Imaging
Abstract
A wide range of Compressive Sensing (CS) frameworks have been proposed to address the task of color and hyperspectral image sampling and reconstruction. Methods for reconstruction of jointly sparse vectors that leverage joint sparsity constraints such as the Multiple Measurement Vector (MMV) approach have been shown to outperform Single Measurement Vector (SMV) frameworks. Recent work has shown that exploiting joint sparsity while simultaneously preserving the high-dimensional structure of the data results in further performance improvements. We introduce a parallelizable extension of a previously proposed serial tensorial MMV approach which, like its predecessor, exploits joint sparsity constraints multiple data dimensions simultaneously, but that is parallelizable in nature. We demonstrate empirically that the proposed method provides better reconstruction fidelity of hyperspectral imagery and that it is also more computationally efficient than the current state of the art.
Year
DOI
Venue
2017
10.1109/CVPRW.2017.33
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
parallel reconstruction,jointly sparse tensors,hyperspectral imaging,compressive sensing frameworks,CS frameworks,hyperspectral image sampling,image reconstruction,sparse vectors,Multiple Measurement Vector,MMV,Single Measurement Vector,SMV frameworks,joint sparsity constraints multiple data dimensions,hyperspectral imagery
Parallelizable manifold,Iterative reconstruction,Fidelity,Multiple data,Pattern recognition,Tensor,Computer science,Matrix decomposition,Algorithm,Hyperspectral imaging,Artificial intelligence,Compressed sensing
Conference
Volume
Issue
ISSN
2017
1
2160-7508
ISBN
Citations 
PageRank 
978-1-5386-0734-3
0
0.34
References 
Authors
12
2
Name
Order
Citations
PageRank
Li Qun13443245.59
Edgar A. Bernal25810.32