Remote sensing image fusion based on dictionary learning and sparse representation

2019 ◽  
Author(s):  
Fei Yin ◽  
Shuhua Cao ◽  
Xiaojie Xu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion

Remote Sensing Image Fusion Based on Non-subsampled Dual-tree Complex Contourlet Transform and Sparse Representation

2016 ◽  
Vol 45 (1) ◽  
pp. 110002
Author(s):  
殷明 YIN Ming ◽  
庞纪勇 PANG Ji-yong ◽  
魏远远 WEI Yuan-yuan ◽  
段普宏 DUAN Pu-hong
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Remote Sensing Image Fusion

Remote Sensing Image Fusion Based on Sparse Representation

2013 ◽  
Vol 33 (4) ◽  
pp. 0428003 ◽  
Author(s):  
尹雯 Yin Wen ◽  
李元祥 Li Yuanxiang ◽  
周则明 Zhou Zeming ◽  
刘世前 Liu Shiqian
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion

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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