Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Image fusion by combining multiwavelet with nonsubsampled direction filter bank

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Aiming to solving the problem of too much reductancy in nonsubsampled contourlet transform and shearlet transform, a new type of transform by combining the multiwavelet transform with nonsubsampled direction filter bank is proposed. Subsequently, a multi-scale-decomposition-based image fusion approach is presented. The pulse coupled neural networks (PCNN) are motivated by the local sum-modified-Laplacian measurement of every subband coefficient. If the coefficients generate larger firing times than the other, the coefficients will be chose to synthesize the fused image. Several experiments are performed on three kinds of images, such as multi-focus images, medical images and multispectral images. The experiments indicate that the proposed fusion method observably outperforms the other multi-scale geometry analysis methods adopting the PCNN, such as the traditional wavelet, NSCT, shearlet and other latest image fusion algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ali FE, El-Dokany I, Saad A, Abd El-Samie F (2010) A curvelet transform approach for the fusion of MR and CT images. J Mod Opt 57(4):273–286

    Article  MATH  Google Scholar 

  • Chai Y, Li H, Qu J (2010) Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain. Opt Commun 283(19):3591–3602

    Article  Google Scholar 

  • Chen J, Ouyang X, Zheng W, Xu J, Zhou J, Yu S (2006) The application of symmetric orthogonal multiwavelets and prefilter technique for image compression. Multimed Tools Appl 29(2):175–187

    Article  Google Scholar 

  • Chihang Z, Xin Z, Qian D, Liye Z (2013) De-noising signal of the quartz flexural accelerometer by multiwavelet shrinkage. Int J Smart Sens Intell Syst 6(1):191–208

    Google Scholar 

  • Das S, Kundu MK (2012) NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency. Med Biol Eng Comput 50(10):1105–1114

    Article  Google Scholar 

  • Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106

    Article  Google Scholar 

  • Eckhorn R, Reitboeck H, Arndt M, Dicke P (1990) Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput 2(3):293–307

    Article  Google Scholar 

  • Ganasala P, Kumar V (2014) CT and MR image fusion scheme in nonsubsampled contourlet transform domain. J Digit Imaging 27(3):407–418

    Article  Google Scholar 

  • Geng P, Wang Z, Zhang Z, Xiao Z (2012) Image fusion by pulse couple neural network with shearlet. Opt Eng 51(6):067005–1

    Article  Google Scholar 

  • Geng P, Huang M, Liu S, Feng J, Bao P (2014) Multifocus image fusion method of ripplet transform based on cycle spinning. Multimed Tools Appl, pp 1–11. doi:10.1007/s11042-014-1942-1

  • Goodman T, Lee S (1994) Wavelets of multiplicity \(r\). Trans Am Math Soc 342(1):307–324

    MathSciNet  MATH  Google Scholar 

  • Huang W, Jing Z (2007) Multi-focus image fusion using pulse coupled neural network. Pattern Recognit Lett 28(9):1123–1132

    Article  Google Scholar 

  • Karthik R, Menaka R, Chellamuthu C (2015) A comprehensive framework for classification of brain tumour images using SVM and curvelet transform. Int J Biomed Eng Technol 17(2):168–177

    Article  Google Scholar 

  • Kong W, Wang B, Lei Y (2015) Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model. Infrared Phys Technol 71:87–98

    Article  Google Scholar 

  • Kumar BKS (2015) Image fusion based on pixel significance using cross bilateral filter. Signal Image Video Process 9(5):1193–1204

    Article  Google Scholar 

  • Lamela H, Ruiz-Llata M (2008) Image identification system based on an optical broadcast neural network and a pulse coupled neural network preprocessor stage. Appl Opt 47(10):B52–B63

    Article  Google Scholar 

  • Li M, Li Y, Wang HM, Zhang K (2010) Fusion algorithm of infrared and visible images based on NSCT and PCNN. Opto-Electron Eng 37(6):90–95

  • Li Y, Po LM, Xu X, Feng L, Yuan F, Cheung CH, Cheung KW (2015) No-reference image quality assessment with shearlet transform and deep neural networks. Neurocomputing 154:94–109

  • Liu K, Guo L, Chen J (2011) Contourlet transform for image fusion using cycle spinning. J Syst Eng Electron 22(2):353–357

    Article  Google Scholar 

  • Ludusan C, Lavialle O (2012) Multifocus image fusion and denoising: a variational approach. Pattern Recognit Lett 33(10):1388–1396

    Article  Google Scholar 

  • Nayar SK, Nakagawa Y (1994) Shape from focus. IEEE Trans Pattern Anal Mach Intell 16(8):824–831

    Article  Google Scholar 

  • Palsson F, Sveinsson JR, Ulfarsson MO, Benediktsson JA (2015) Model-based fusion of multi-and hyperspectral images using PCA and wavelets. IEEE Trans Geosci Remote Sens 53(5):2652–2663

    Article  Google Scholar 

  • Wang Z, Ma Y, Gu J (2010) Multi-focus image fusion using PCNN. Pattern Recognit 43(6):2003–2016

    Article  MATH  Google Scholar 

  • Wen XB, Zhang H, Xu XQ, Quan JJ (2009) A new watermarking approach based on probabilistic neural network in wavelet domain. Soft Comput 13(4):355–360

    Article  Google Scholar 

  • Xiao-Bo Q, Jing-Wen Y, Hong-Zhi X, Zi-Qian Z (2008) Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Autom Sin 34(12):1508–1514

    Article  Google Scholar 

  • Yu Z, Yan L, Han N, Liu J (2015) Image fusion algorithm based on contourlet transform and PCNN for detecting obstacles in forests. Cybern Inf Technol 15(1):116–125

    Google Scholar 

  • Zhang L, Fang ZJ, Wang SQ, Yang F, Liu GD (2009) Multiwavelet adaptive denoising method based on genetic algorithm. J Infrared Millim Waves 1:018

    Google Scholar 

  • Zhong F, Ma Y, Li H (2014) Multifocus image fusion using focus measure of fractional differential and NSCT. Pattern Recognit Image Anal 24(2):234–242

    Article  Google Scholar 

  • Zhou D, Gao C, Guo Y (2014) A coarse-to-fine strategy for iterative segmentation using simplified pulse-coupled neural network. Soft Comput 18(3):557–570

    Article  Google Scholar 

Download references

Acknowledgments

Some of the images adopted in these experiments are downloaded from the website of http://www.imagefusion.org. The code of Qu’s method can be acquired from http://www.quxiaobo.org/. This research was partially sponsored by the Natural Science Fund of Hebei Province under Grant F2013210094 and F2013210109. This research was partially sponsored by the National Natural Science Fund under Grant 61572063 and 61401308.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, People’s Republic of China

    Geng Peng, Zhengyou Wang & Shanna Zhuang

  2. College of Electronic and Information Engineering, Hebei University, Baoding, 071000, People’s Republic of China

    Shuaiqi Liu

Authors
  1. Geng Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengyou Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuaiqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shanna Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Geng Peng.

Ethics declarations

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, G., Wang, Z., Liu, S. et al. Image fusion by combining multiwavelet with nonsubsampled direction filter bank. Soft Comput 21, 1977–1989 (2017). https://doi.org/10.1007/s00500-015-1893-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1893-0

Keywords

Search

Navigation