Multi- focus Image Fusion Based on Convolution Neural Network in Non-sampled Shearlet Domain
[1]Shuaiqi Liu,Wang Jie,An Yanling,et al.Multi- focus Image Fusion Based on Convolution Neural Network in Non-sampled Shearlet Domain[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):7-.[doi:10.13705/j.issn.1671-6833.2019.04.002]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40卷
Number of periods:
2019 04
Page number:
7-
Column:
Public date:
2019-07-10
- Title:
- Multi- focus Image Fusion Based on Convolution Neural Network in Non-sampled Shearlet Domain
- Keywords:
- image fusion; Multi-focus image fusion; non-subsampled shearlet transform; convolutional neural network; guide filter
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.04.002
- Abstract:
- In this paper, a new multi-focus image fusion algorithm is proposed based on convolution neural network in non-subsampled Shearlet (NSST) domain by using the advantages of time-frequency of NSST. Firstly, the source image is decomposed by NSST. Secondly, the fusion strategy based on the convolution neural network (CNN) is applied to the low frequency coefficients of the decomposition. Then, the improved weighted sum of Laplace energy based on the guided filtering are carried out to the high-frequency coefficients of the decomposition. Finally, the fused image can be gotten by inverse NSST transform. The algorithm fully preserves the information of the source image and improves the continuity of the image space. Experimental results show that the fusion algorithm can not only achieve better visual effects, but also improve its objective evaluation index.
- References:
- -
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Last Update:
2019-07-29