Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

(E-pub Ahead of Print)

Author(s): Liu Xian-Hong, Chen Zhi-Bin*.

Journal Name: Recent Advances in Electrical & Electronic Engineering


Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter.

Method: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously.

Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations.

Keywords: Image fusion, Fast guided filter, Convolutional Sparse Representation, Nonsubsampled Directional Filter Bank, Pulse Coupled Neural Network, Sum of Modified Laplacian.

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(E-pub Ahead of Print)
DOI: 10.2174/2352096511666180607101743
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