scholarly journals Shadow Separation of Pavement Images Based on Morphological Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Changxia Ma ◽  
Heng Zhang ◽  
Bing Keong Li
Keyword(s):  
Sparse Representation ◽  
Error Detection ◽  
Geometric Structure ◽  
Component Analysis ◽  
Separation Algorithm ◽  
Morphological Component Analysis ◽  
Morphological Component ◽  
Overcomplete Dictionary ◽  
Texture Structure ◽  
Crack Recognition

The shadow of pavement images will affect the accuracy of road crack recognition and increase the rate of error detection. A shadow separation algorithm based on morphological component analysis (MCA) is proposed herein to solve the shadow problem of road imaging. The main assumption of MCA is that the image geometric structure and texture structure components are sparse within a class under a specific base or overcomplete dictionary, while the base or overcomplete dictionaries of each sparse representation of morphological components are incoherent. Thereafter, the corresponding image signal is transformed according to the dictionary to obtain the sparse representation coefficients of each part of the information, and the coefficients are shrunk by soft thresholding to obtain new coefficients. Experimental results show the effectiveness of the shadow separation method proposed in this paper.

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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