Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

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

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

Volume 12, Issue 3, 2019

Page: [263 - 269] Pages: 7

DOI: 10.2174/2352096511666180607101743

Price: $65

Abstract

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.

Methods: 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.

Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

Keywords: Image fusion, fast guided filter, convolutional sparse representation, nonsubsampled directional filter bank, pulse coupled neural network, new sum of modified laplacian.

« Previous Next »
Graphical Abstract

[1]
G. Piella, "A general framework for multiresolution image fusion: From pixels to regions", Inf. Fusion, vol. 4, pp. 259-280, 2003.
[2]
S. Li, X. Kang, L. Fang, J. Hu, and H. Yin, "Pixel-level image fusion: A survey of the state of the art", Inf. Fusion, vol. 33, pp. 100-112, 2017.
[3]
H. Zhou, Q. Cheng, and M. Zargham, "Fast fusion of medical images based on Bayesian risk minimization and Pixon Map", In: International Conference on Computational Science and Engineering Vancouver, BC, Canada 2009, pp.1086-1091.
[4]
S. Liu, J. Zhao, and M. Shi, Medical image fusion based on rolling guidance filter and spiking cortical model.Computat. Mathemat. Methods Med, . pp.156043, 2015.
[5]
Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, "Edge-preserving decompositions for multi-scale tone and detail manipulation", In: Proceeding, SIGGRAPH ’08 ACM SIGGRAPH ACM Los Angeles, California, USA 2008, 27, pp. 1-10.
[6]
J. Zhao, Q. Zhou, Y. Chen, H. Feng, Z. Xu, and Q. Li, "Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition", Infrared Phys. Technol., vol. 56, pp. 93-99, 2013.
[7]
J. Zhao, H. Feng, Z. Xu, Q. Li, and T. Liu, "Detail enhanced multi-source fusion using visual weight map extraction based on multi scale edge preserving decomposition", Optics Commun., Vol. 287, pp. 45-52, 2013.
[8]
J. Hu, and S. Li, "The multiscale directional bilateral filter and its application to multisensor image fusion", Inf. Fusion, vol. 13, no. 3, pp. 196-206, 2012.
[9]
S. Li, X. Kang, and J. Hu, "Image fusion with guided filtering", IEEE Trans. Image Process., vol. 22, no. 7, pp. 2864-2875, 2013.
[10]
Z. Zhou, B. Wang, S. Li, and M. Dong, "Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters", Inf. Fusion, vol. 30, pp. 15-26, 2016.
[11]
B. Yang, and S. Li, "Multifocus image fusion and restoration with sparse representation", IEEE Trans. Instrum. Meas., vol. 59, no. 4, pp. 884-892, 2010.
[12]
Y. Liu, S. Liu, and Z. Wang, "A general framework for image fusion based on multi-scale transform and sparse representation", Inform. Fus., Vol. 24, pp.1 47-164, 2015.
[13]
M. Elad, and I. Yavneh, "A plurality of sparse representations is better than the sparsest one alone", IEEE Trans. Inf. Theory, vol. 55, no. 10, pp. 4701-4714, 2009.
[14]
S. Liu, and Y. Fang, "“Infrared image fusion algorithm based on contourlet transform and improved pulse coupled neural network”, ‎J. Infrared Millim", Terahertz Waves,, vol. 26, no. 3, pp. 217-221, 2007.
[15]
P. Geng, Z. Wang, Z. Zhang, and Z. Xiao, "Image fusion by pulse couple neural network with shearlet", Opt. Eng., vol. 51, no. 6, p. 7005, 2012.
[16]
X. Qu, J. Yan, H. Xiao, and Z. Zhu, "Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain", Acta Automatica Sinica, vol. 34, no. 12, pp. 1508-1514, 2008.
[17]
W. Huang, and Z. Jing, "Evaluation of focus measures in multi-focus image fusion", Pattern Recognit. Lett., vol. 28, no. 4, pp. 493-500, 2007.
[18]
K. He, J. Sun, and X. Tang, "Guided image filtering. IEEE transactions on pattern analysis and machine intelligence", Vol. 35, no. 6, pp. 1397-1409, 2013.
[19]
C.C. Kao, J.H. Lai, and S.Y. Chien, "Vlsi architecture design of guided filter for 30 frames/s full-hd video", IEEE Trans. Circ. Syst. Video Tech., vol. 24, no. 3, pp. 513-524, 2014.
[20]
K. He, and J. Sun, "Fast guided filter", arXiv preprint arXiv:1505.00996, 2015.
[21]
B. Wohlberg, "Efficient algorithms for convolutional sparse representations", IEEE Trans. Image Process., vol. 25, no. 1, pp. 301-315, 2016.
[22]
R. Eckhorn, H.J. Reitboeck, M. Arndt, and P. Dicke, "Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex", Neural Comput., vol. 2, no. 3, pp. 293-307, 2014.
[23]
A.L.D. Cunha, J.P. Zhou, and M.N. Do, "The nonsubsampled contourlet transform: Theory, design, and applications", IEEE Trans. Image Process., vol. 15, no. 10, pp. 3089-3101, 2006.
[24]
X.L. Zhang, X.F. Li, and J. Li, "Validation and correlation analysis of metrics for evaluating performance of image fusion", Acta Automatica Sinica, vol. 40, no. 2, pp. 306-315, 2014.

Rights & Permissions Print Cite
Article Metrics
16
5
© 2024 Bentham Science Publishers | Privacy Policy