scholarly journals Decomposition of Dynamic Textures Using Morphological Component Analysis: A New Adaptative Strategy

2010 ◽  
Author(s):  
Sloven Dubois ◽  
Renaud Peteri ◽  
Michel Menard
Keyword(s):  
Component Analysis ◽  
Dynamic Textures ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Adaptative Strategy

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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