[1]WANG Jian,WU Xisheng.Image fusion based on the improved sparse representation and PCNN[J].CAAI Transactions on Intelligent Systems,2019,14(5):922-928.[doi:10.11992/tis.201805045]
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Image fusion based on the improved sparse representation and PCNN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 922-928 Column: 学术论文—机器学习 Public date: 2019-09-05
Title:
Image fusion based on the improved sparse representation and PCNN
Author(s):
WANG Jian;  WU Xisheng
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
image processing; image fusion; NSST; sparse representation; adaptive learning dictionary; joint dictionary; PCNN; improved spatial frequency
CLC:
TP391.2
DOI:
10.11992/tis.201805045
Abstract:
To improve the clarity of image fusion, in this paper, we propose an image-fusion algorithm based on improved sparse representation and a pulse-coupled neural network (PCNN). First, using a non-subsampled shearlet transform (NSST), source images are decomposed into low-frequency and high-frequency sub-band coefficients, which contain different information. Then, we use the K-singular value decomposition algorithm to fuse the improved sparse representation with low-frequency sub-band coefficients and construct a joint dictionary from the adaptive learning multiple sub-dictionaries in the source images. The high-frequency sub-band coefficients are fused with the improved PCNN. To stimulate the PCNN model, we use the modified spatial frequency as neuron feedback input. The high-frequency coefficients are selected according to the fusion rule for the maximum amplitude of fire output. Finally, we reconstruct the fused image with the NSST inverse transform of the fused low-frequency and high-frequency sub-band coefficients. The experimental results show that the proposed algorithm preserves the edge information of the source images very well; additionally, the fused image achieves good results on the evaluation criteria, thus verifying the effectiveness of the proposed algorithm.
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