Image fusion method based on spatially masked convolutional sparse representation

2019 ◽  
Vol 90 ◽  
pp. 103806 ◽  
Author(s):  
Changda Xing ◽  
Zhisheng Wang ◽  
Quan Ouyang ◽  
Chong Dong ◽  
Chaowei Duan
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Fusion Method ◽  
Image Fusion Method

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

scholarly journals Image fusion method based on simultaneous sparse representation with non‐subsampled contourlet transform

2019 ◽  
Vol 13 (2) ◽  
pp. 240-248 ◽  
Author(s):  
Guiqing He ◽  
Siyuan Xing ◽  
Xingjian He ◽  
Jun Wang ◽  
Jianping Fan
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Image Fusion Method

scholarly journals Multi-focus Image Fusion Based on Decision Map and Sparse Representation

2019 ◽  
Vol 9 (17) ◽  
pp. 3612
Author(s):  
Liao ◽  
Chen ◽  
Mo
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Focal Length ◽  
The Other ◽  
Fusion Method ◽  
Optical Lens ◽  
Transitional Area ◽  
Fusion Methods ◽  
Focus Image ◽  
Image Fusion Method

As the focal length of an optical lens in a conventional camera is limited, it is usually arduous to obtain an image in which each object is focused. This problem can be solved by multi-focus image fusion. In this paper, we propose an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR). First, we obtained a decision map by analyzing low-scale images with sparse representation, measuring the effective clarity level, and using spatial frequency methods to process uncertain areas. Subsequently, the transitional area around the focus boundary was determined by the decision map, and we implemented the transitional area fusion based on sparse representation. The experimental results show that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation.

A novel multi-modality image fusion method based on image decomposition and sparse representation

2018 ◽  
Vol 432 ◽  
pp. 516-529 ◽  
Author(s):  
Zhiqin Zhu ◽  
Hongpeng Yin ◽  
Yi Chai ◽  
Yanxia Li ◽  
Guanqiu Qi
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Image Decomposition ◽  
Fusion Method ◽  
Image Fusion Method

scholarly journals An Image Fusion Method Based on Sparse Representation and Sum Modified-Laplacian in NSCT Domain

Entropy ◽  
2018 ◽  
Vol 20 (7) ◽  
pp. 522 ◽  
Author(s):  
Yuanyuan Li ◽  
Yanjing Sun ◽  
Xinhua Huang ◽  
Guanqiu Qi ◽  
Mingyao Zheng ◽  
...  
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Fusion Method ◽  
Image Fusion Method

scholarly journals Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 303 ◽  
Author(s):  
Xiaole Ma ◽  
Shaohai Hu ◽  
Shuaiqi Liu ◽  
Jing Fang ◽  
Shuwen Xu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Fusion Method ◽  
Remote Sensing Image Fusion ◽  
Guided Filtering ◽  
Fused Image ◽  
Image Fusion Method

In this paper, a remote sensing image fusion method is presented since sparse representation (SR) has been widely used in image processing, especially for image fusion. Firstly, we used source images to learn the adaptive dictionary, and sparse coefficients were obtained by sparsely coding the source images with the adaptive dictionary. Then, with the help of improved hyperbolic tangent function (tanh) and l 0 − max , we fused these sparse coefficients together. The initial fused image can be obtained by the image fusion method based on SR. To take full advantage of the spatial information of the source images, the fused image based on the spatial domain (SF) was obtained at the same time. Lastly, the final fused image could be reconstructed by guided filtering of the fused image based on SR and SF. Experimental results show that the proposed method outperforms some state-of-the-art methods on visual and quantitative evaluations.

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