Infrared and visible image fusion based on convolutional sparse representation and guided filtering

2021 ◽  
Vol 30 (04) ◽  
Author(s):  
Yansong Zhu ◽  
Yixiang Lu ◽  
Qingwei Gao ◽  
Dong Sun
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Visible Image ◽  
Guided Filtering

Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation

2020 ◽  
Vol 39 (3) ◽  
pp. 4617-4629
Author(s):  
Chengrui Gao ◽  
Feiqiang Liu ◽  
Hua Yan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Low Frequency ◽  
Infrared Images ◽  
Complex Wavelet Transform ◽  
Visible Image ◽  
Visible Images ◽  
Complex Wavelet

Infrared and visible image fusion refers to the technology that merges the visual details of visible images and thermal feature information of infrared images; it has been extensively adopted in numerous image processing fields. In this study, a dual-tree complex wavelet transform (DTCWT) and convolutional sparse representation (CSR)-based image fusion method was proposed. In the proposed method, the infrared images and visible images were first decomposed by dual-tree complex wavelet transform to characterize their high-frequency bands and low-frequency band. Subsequently, the high-frequency bands were enhanced by guided filtering (GF), while the low-frequency band was merged through convolutional sparse representation and choose-max strategy. Lastly, the fused images were reconstructed by inverse DTCWT. In the experiment, the objective and subjective comparisons with other typical methods proved the advantage of the proposed method. To be specific, the results achieved using the proposed method were more consistent with the human vision system and contained more texture detail information.

Infrared and visible image fusion using guided filter and convolutional sparse representation

2018 ◽  
Vol 26 (5) ◽  
pp. 1242-1253 ◽  
Author(s):  
刘先红 LIU Xian-hong ◽  
陈志斌 CHEN Zhi-bin ◽  
秦梦泽 QIN Meng-ze
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Guided Filter ◽  
Visible Image

The infrared and visible image fusion algorithm based on target separation and sparse representation

2014 ◽  
Vol 67 ◽  
pp. 397-407 ◽  
Author(s):  
Xiaoqi Lu ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
He Liu ◽  
Haiquan Pei
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Fusion Algorithm ◽  
Visible Image

scholarly journals Medical Image Fusion Based on Sparse Representation and Guided Filtering

2019 ◽  
Vol 1302 ◽  
pp. 022045
Author(s):  
Sa Huang ◽  
Guangyu Chu ◽  
Yifan Fei ◽  
Xiaoli Zhang ◽  
Hailiang Wang
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Guided Filtering

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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