Skip to main content
SpringerLink
Log in

Infrared Polarization and Intensity Image Fusion Algorithm Based on the Feature Transfer

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

The features of infrared polarization and intensity images are not finely transferred to the fused image by using traditional fusion algorithms, which leads to a severe blur of the fused image. This study proposes a new infrared polarization and intensity image fusion algorithm based on the feature transfer. First, the contrast features of the infrared polarization image are extracted by the multiscale average filter decomposition with help of standard deviation constraint. The texture features of infrared polarization images are retrieved via non-subsample-shearlet transform at the same time. Second, the difference of the features is measured using the similarity index, which is used as the transfer weight for the infrared polarization feature images during the later phase of the image fusion. Finally, the fused image is obtained by the superimposition of the infrared intensity image and feature images, which are created from the infrared polarization image. The experimental results demonstrated that the proposed method is able to transfer the features of both the infrared intensity image and the polarization image into the fused images. It performs well on both subjective and objective image quality.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yang, F.B., Infrared Physics and Technology, Beijing: Publishing House of Electronics Industry, 2014.

    Google Scholar 

  2. Duan, J., Qiang, Q., Mo, C.H., et al., Review of polarization imaging technology for international military application, Infrared Technol., 2014, vol. 36, no. 3, pp. 190–195.

    Google Scholar 

  3. Liang, J., Zhang, W., Ren, L., et al., Polarimetric dehazing method for visibility improvement based on visible and infrared image fusion, Appl. Opt., 2016, vol. 55, no. 29, p. 8221.

    Article  Google Scholar 

  4. Wang, X., Qiu, R.Q., Jin, Q.W., et al., Technology progress of infrared polarization imaging detection, Infrared Laser Eng., 2014, vol. 43, no. 10, pp. 3176–3182.

    Google Scholar 

  5. Li, S.T. and Yang, B., Hybrid multiresolution method for multisensor multimodal image fusion, IEEE Sensors J., 2010, vol. 10, no. 9, pp. 1519–1526.

    Article  Google Scholar 

  6. Desale, R.P. and Verma, S.V., Study and analysis of PCA, DCT & DWT based image fusion techniques, International Conference on Signal Processing Image Processing & Pattern Recognition, 2013, pp. 66–69.

    Google Scholar 

  7. Mehra, I. and Nishchal, N.K., Image fusion using wavelet transform and its application to asymmetric cryptosystem and hiding, Opt. Express, 2014, vol. 22, no. 5, p. 5474.

    Article  Google Scholar 

  8. Ghassemian, H., A review of remote sensing image fusion methods, Inf. Fusion, 2016, vol. 32, pp. 75–89.

    Article  Google Scholar 

  9. Li, H.F., Qiu, H.M., Yu, Z.T., et al., Infrared and visible image fusion scheme based on NSCT and low-level visual features, Infrared Phys. Technol., 2016, no. 76, pp. 174–184.

    Article  Google Scholar 

  10. Zhao, C.H., Guo, Y.T., and Wang, F., A fast fusion scheme for infrared and visible light images in NSCT domain, Infrared Phys. Technol., no. 72, 2015, pp. 266–275.

    Article  Google Scholar 

  11. Wang, J., Lai, S., and Li, M., Improved image fusion method based on NSCT and accelerated NMF, Sensors, 2012, vol. 12, no. 12, pp. 5872–5887.

    Article  Google Scholar 

  12. Guo, R.D., Zhang, J.J., and Wang, F., The algorithm of image fusion for infrared polarization based on contourlet transform and Teager operator, Proc. of SPIE, 2015, pp. 1–8.

    Google Scholar 

  13. Kong, W.W. and Liu, J.P., Technique for image fusion based on NSST domain improved fast non-classical RF, Infrared Phys. Technol., 2013, no. 61, pp. 27–36.

    Article  Google Scholar 

  14. Zhang, B.H., Lu, X.Q., H.Q., et al., A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled Shearlet transform, Infrared Phys. Technol., 2015, no. 73, pp. 286–297.

    Article  Google Scholar 

  15. Wang, Z.S., Yang, B.F., and Peng, Z.H., Multi-sensor image enhanced fusion algorithm based on NSST and top-hat transformation, Optik, 2015, vol. 126, no. 23, pp. 4184–4190.

    Article  Google Scholar 

  16. Zhou, Q., Zhao, J.F., Fen, H.J., et al., Infrared polarization image fusion with non-sampling shearlets, J. Zhejiang Univ. (Eng. Sci.), 2014, vol. 48, no. 8, pp. 1508–1516.

    Google Scholar 

  17. Hui, X., Hui, Y.Y., Kang, C.B., et al., Image fusion based on complex wavelets and region segmentation, International Conference on Computer Application and System Modeling, 2010, vol. 8, pp. 135–139.

    Google Scholar 

  18. Sun, Y.C., Hu, S.H., Liu, S.Q., and Wei, S., Infrared and visible image fusion based on object extraction and fuzzy logic via complex shearlet transform, IJAMML, 2014, vol. 1, no. 2, pp. 197–220.

    Google Scholar 

  19. Zhang, J.J., Yong, Y.H., and Ning, X.N., Polarimetric image fusion algorithms based on wavelet transform and performance evaluation, J. Hefei Univ. Technol., 2009, vol. 32, no. 7, pp. 1101–1105.

    Google Scholar 

  20. Yang, F.B. and Wei, H., Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules, Infrared Phys. Technol., 2013, vol. 60, no. 1, pp. 235–243.

    Article  Google Scholar 

  21. Aslantas, V. and Toprak, A.N., A pixel based multi-focus image fusion method, Opt. Commun., 2014, no. 4, pp. 350–358.

    Article  Google Scholar 

  22. Yue, Z. and Li, F.M., An infrared polarization image fusion algorithm based on oriented Laplacian pyramid, Proc. of SPIE, 2014, pp. 1–11.

    Google Scholar 

  23. Li, S.Y. and Yang, G.B., Feature Extraction and Image Processing, Beijing: Publishing House of Electronics Industry, 2010, 2nd ed.

    Google Scholar 

  24. Yan, X., Qin, H., Li, J., et al., Infrared and visible image fusion using multiscale directional nonlocal means filter, Appl. Opt., 2015, vol. 54, no. 13, p. 4299.

    Article  Google Scholar 

  25. Li, S.T., Yang, B., and Hu, J.W., Performance comparison of different multi-resolution transforms for image fusion, Inf. Fusion, 2010, vol. 12, no. 3, pp. 74–84.

    MathSciNet  Google Scholar 

  26. Wang, Z., Bovik, A.C., Sheikh, H.R., et al., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 2004, vol. 13, no. 4, pp. 1–14.

    Article  Google Scholar 

  27. Petrović, V. and Dimitrijević, V., Focused pooling for image fusion evaluation, Inf. Fusion, 2015, vol. 22, no. 4, pp. 119–126.

    Article  Google Scholar 

  28. Zhang, X.L., Li, X.F., and Li, J., Validation and correlation analysis of metrics for evaluating performance of image fusion, Acta Autom. Sin., 2014, vol. 40, no. 2, pp. 307–315.

    MathSciNet  Google Scholar 

  29. Aslantas, V. and Bendes, E., A new image quality metric for image fusion: The sum of the correlations of differences, Int. J. Electron. Commun., 2015, vol. 69, no. 3, pp. 1890–1896.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China

    Lei Zhang, Feng bao Yang & Linna Ji

  2. Nan Yang Normal University, Nanyang, 473061, China

    Lei Zhang

Authors
  1. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng bao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linna Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng bao Yang.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Yang, F.b. & Ji, L. Infrared Polarization and Intensity Image Fusion Algorithm Based on the Feature Transfer. Aut. Control Comp. Sci. 52, 135–145 (2018). https://doi.org/10.3103/S0146411618020049

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411618020049

Keywords

Search

Navigation