GDL

Multi-focus Image Fusion with Sparse Feature Based Pulse Coupled Neural Network

Journal from gdlhub / 2016-11-12 06:31:19
By : Yongxin Zhang, Li Chen, Zhihua Zhao, Jian Jia, STIKOM Dinamika Bangsa Jambi
Created : 2014-06-01, with 1 files

Keyword : image fusion;robust principal component analysis; pulse-coupled neural network; sparse feature;firing times
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/66

In order to better extract the focused regions and effectively improve the quality of the fused image, a novel multi-focus image fusion scheme with sparse feature based pulse coupled neural network (PCNN) is proposed. The registered source images are decomposed into principal matrices and sparse matrices by robust principal component analysis (RPCA). The salient features of the sparse matrices construct the sparse feature space of the source images. The sparse features are used to motivate the PCNN neurons. The focused regions of the source images are detected by the output of the PCNN and integrated to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in both spatial and transform domain.

Description Alternative :

In order to better extract the focused regions and effectively improve the quality of the fused image, a novel multi-focus image fusion scheme with sparse feature based pulse coupled neural network (PCNN) is proposed. The registered source images are decomposed into principal matrices and sparse matrices by robust principal component analysis (RPCA). The salient features of the sparse matrices construct the sparse feature space of the source images. The sparse features are used to motivate the PCNN neurons. The focused regions of the source images are detected by the output of the PCNN and integrated to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in both spatial and transform domain.

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