Ground-roll separation of seismic data based on morphological component analysis in two-dimensional domain

2016 ◽  
Vol 13 (1) ◽  
pp. 116-126 ◽  
Author(s):  
Xiao-Hong Xu ◽  
Guang-Zhong Qu ◽  
Yang Zhang ◽  
Yun-Yun Bi ◽  
Jin-Ju Wang
Keyword(s):  
Seismic Data ◽  
Component Analysis ◽  
Two Dimensional ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Ground Roll ◽  
Dimensional Domain

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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