Restricted isometry constant improvement based on a singular value decomposition-weighted measurement matrix for compressed sensing

2017 ◽  
Vol 11 (11) ◽  
pp. 1706-1718 ◽  
Author(s):  
Qian Wang ◽  
Gangrong Qu
Keyword(s):  
Singular Value Decomposition ◽  
Compressed Sensing ◽  
Singular Value ◽  
Measurement Matrix ◽  
Restricted Isometry Constant ◽  
Value Decomposition ◽  
Constant Improvement

scholarly journals Image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix

2018 ◽  
Vol 13 ◽  
pp. 174830181879151
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Compressive Sensing ◽  
Color Image ◽  
Singular Value ◽  
Measurement Matrix ◽  
Fusion Algorithm ◽  
Full Color ◽  
Value Decomposition ◽  
Time And Space Complexity

To solve the problem of high time and space complexity of traditional image fusion algorithms, this paper elaborates the framework of image fusion algorithm based on compressive sensing theory. A new image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix is proposed. This proposed algorithm only acts on a small amount of measurement data after compressive sensing sampling, which greatly reduces the number of pixels involved in the fusion and improves the time and space complexity of fusion. In the fusion experiments of full-color image with multispectral image, infrared image with visible light image, as well as multispectral image with full-color image, this proposed algorithm achieved good experimental results in the evaluation parameters of information entropy, standard deviation, average gradient, and mutual information.

Compressed sensing MRI with singular value decomposition-based sparsity basis

2011 ◽  
Vol 56 (19) ◽  
pp. 6311-6325 ◽  
Author(s):  
Mingjian Hong ◽  
Yeyang Yu ◽  
Hua Wang ◽  
Feng Liu ◽  
Stuart Crozier
Keyword(s):  
Singular Value Decomposition ◽  
Compressed Sensing ◽  
Singular Value ◽  
Value Decomposition
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