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


Liu Xian-Hong and Chen Zhi-Bin*   Pages 1 - 7 ( 7 )


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.


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


Department of Electronics and Optics engineering, Army Engineering University, Shijiazhuang 050003, No. 32181 Troop, Chinese People`s Liberation Army, P.O. Box: 050000, Shijiazhuang

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