scholarly journals Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 263 ◽  
Author(s):  
Qing Li ◽  
Wei Hu ◽  
Erfei Peng ◽  
Steven Liang
Keyword(s):  
Wavelet Transform ◽  
Component Analysis ◽  
Q Factor ◽  
Rotating Machines ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Multichannel Signals

scholarly journals Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis

2015 ◽  
Vol 9 ◽  
Author(s):  
Tarek Lajnef ◽  
Sahbi Chaibi ◽  
Jean-Baptiste Eichenlaub ◽  
Perrine M. Ruby ◽  
Pierre-Emmanuel Aguera ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Component Analysis ◽  
Q Factor ◽  
Sleep Spindle ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Complex Detection

scholarly journals SZ and CMB reconstruction using generalized morphological component analysis

2008 ◽  
Vol 5 (4) ◽  
pp. 307-317 ◽  
Author(s):  
J. Bobin ◽  
Y. Moudden ◽  
J.-L. Starck ◽  
J. Fadili ◽  
N. Aghanim
Keyword(s):  
Component Analysis ◽  
Morphological Component Analysis ◽  
Morphological Component

Multi-Modal Image Fusion via Convolutional Morphological Component Analysis and Guided Filter

2020 ◽  
pp. 2130003
Author(s):  
Peng Guo ◽  
Guoqi Xie ◽  
Renfa Li ◽  
Hui Hu
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Component Analysis ◽  
Guided Filter ◽  
Learning Technology ◽  
The Past ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Fusion Scheme ◽  
Image Fusion Method

In feature-level image fusion, deep learning technology, particularly convolutional sparse representation (SR) theory, has emerged as a new topic over the past three years. This paper proposes an effective image fusion method based on convolution SR, namely, convolutional sparsity-based morphological component analysis and guided filter (CS-MCA-GF). The guided filter operator and choose-max coefficient fusion scheme introduced in this method can effectively eliminate the artifacts generated by the morphological components in the linear fusion, and maintain the pixel saliency of the source images. Experiments show that the proposed method can achieve an excellent performance in multi-modal image fusion, which includes medical image fusion.

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