Xigui Zheng  1,2

1School of Mechanical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450000,China
2Henan Digital Intelligent Equipment Engineering Research Center


 

Received: December 12, 2020
Accepted: May 17, 2021
Publication Date: June 24, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202112_24(6).0017  


ABSTRACT


In the traditional infrared and visible image fusion algorithms, there are some problems such as week target, missing background, blur edge information and so on. Therefore, we propose a new infrared and visible image fusion algorithm based on a dual-channel spiking cortical model (DCSCM) via LatLRR in this paper. First, a non-down-sampling shearlet transform (NSST) is applied to the source image to ob-tain the corresponding low-frequency and high-frequency components. Then, the LatLRR method and DCSCM model are used to fuse the low and high frequency component, respectively. Finally, NSST in-verse transformation is performed for the fused components to obtain the final fused image. Compared with other state-of-the-art methods, the experiment results on different scenarios show that the pro-posed algorithm has better visual effect, and higher objective evaluation values. And it can play an im-portant role in Micro CNC engraving milling machine intelligent knife setting.


Keywords: Infrared and visible image fusion, dual-channel spiking cortical model, LatLRR, NSST, machine intelli-gent


REFERENCES


  1. [1] S. Yin and Y. Zhang. Singular value decomposition-based anisotropic diffusion for fusion of infrared and visible images. 2019. DOI: 10.1080/19479832.2018.1487886.
  2. [2] R. Olmos, S. Tabik, A. Lamas, F. Pérez-Hernández, and F. Herrera, (2019) “A binocular image fusion approach for minimizing false positives in handgun detection with deep learning" Information Fusion 49: 271–280. DOI: 10.1016/j.inffus.2018.11.015.
  3. [3] J. Reena Benjamin and T. Jayasree, (2018) “Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms" International Journal of Computer Assisted Radiology and Surgery 13(2): 229–240. DOI: 10.1007/s11548-017-1692-4.
  4. [4] S. Yin, J. Liu, and H. Li, (2018) “A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery" 3D Research 9(4): DOI: 10.1007/s13319- 018-0204-9.
  5. [5] L. Yang, B. L. Guo, andW. Ni, (2008) “Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform" Neurocomputing 72(1-3): 203–211. DOI: 10.1016/j.neucom.2008.02.025.
  6. [6] Q. Xing, D. Wang, and K. Bi, (2014) “Fusion technique for images based on non-subsampled contourlet transform and compressive sensing" Kongzhi yu Juece/Control and Decision 29(4): 585–592.
  7. [7] J. Adu, M. Wang, Z. Wu, and Z. Zhou, (2012) “Multifocus image fusion based on the non-subsampled contourlet transform" Journal of Modern Optics 59(15):1355–1362. DOI: 10.1080/09500340.2012.714802.
  8. [8] Y. Wu and Z. Wang, (2017) “Infrared and Visible Image Fusion Based on Target Extraction and Guided Filtering Enhancement [J]" Acta Optica Sinica 37(8): 91–101. DOI: 10.3788/AOS201737.0810001.
  9. [9] J. Liu, Y. J. Lei, and Y. Q. Xing, (2017) “Innovative image fusion method based on improved NSST" Control and Decision 32(2): 275–280. DOI: 10.13195/j.kzyjc. 2016.0075.
  10. [10] M.-l. Li, Y.-J. Li, H. Wang, and K. Zhang, (2010) “Fusion algorithm of infrared and visible images based on NSCT and PCNN" Opto-Electronic Engineering 37(6): 90–95. DOI: 10.3969/j.issn.1003-501X.2010.06.016.
  11. [11] Z. Song, H. Jiang, and S. Li, (2017) “A novel fusion framework based on adaptive PCNN in NSCT domain for whole-body PET and CT images" Computational and Mathematical Methods in Medicine 2017: DOI: 10.1155/2017/8407019.
  12. [12] N.Wang, Y. Ma, W. Wang, and S. Zhou, (2014) “An image fusion method based on NSCT and dual-channel PCNN model" Journal of Networks 9(2): 501–506.DOI: 10.4304/jnw.9.2.501-506.
  13. [13] H. Tao and X. Lu. “Smoke vehicle detection based on multi-feature fusion and hidden Markov model”. In: Journal of Real-Time Image Processing. 17. 3. Springer, 2020, 745–758. DOI: 10.1007/s11554-019-00856-z.
  14. [14] W. Kong, B. Wang, and Y. Lei, (2015) “Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model" Infrared Physics and Technology 71: 87–98. DOI: 10.1016/j. infrared.2015.02.008.
  15. [15] L. Junwu, B. Li, and Y. Jiang, (2020) “An Infrared and Visible Image Fusion Algorithm Based on LSWTNSST" IEEE Access 8: 179857–179880. DOI: 10.1109/ ACCESS.2020.3028088.
  16. [16] C. Zhao, Y. Huang, and S. Qiu, (2019) “Infrared and visible image fusion algorithm based on saliency detection and adaptive double-channel spiking cortical model" Infrared Physics and Technology 102: DOI: 10.1016/j. infrared.2019.102976.
  17. [17] X. Xing, C. Liu, C. Luo, and T. Xu, (2020) “Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition" Eurasip Journal on Wireless Communications and Networking 2020(1): DOI: 10. 1186/s13638-020-01774-6.
  18. [18] Z. Zhang, S. Yan, and M. Zhao. “Robust image representation and decomposition by Laplacian regularized latent low-rank representation”. In: Proceedings of the International Joint Conference on Neural Networks. 2013. DOI: 10.1109/IJCNN.2013.6707051.
  19. [19] H. Li and X. J. Wu. Infrared and visible image fusion using Latent Low-Rank Representation. 2018. arXiv: 1804.08992.
  20. [20] Z. Jiang, H.Wu, and X. Zhou, (2018) “Infrared and Visible Image Fusion Algorithm Based on Improved Guided Filtering and Dual-Channel Spiking Cortical Model" Guangxue Xuebao/Acta Optica Sinica 38(2): DOI: 10.3788/AOS201838.0210002.
  21. [21] Y. L. Qiao, X. Y. Gao, and C. Y. Song. “Near Infrared, Long-Wave Infrared and Visible Image Fusion Based on Oversampled Graph Filter Banks”. In: Lecture Notes in Electrical Engineering. 590. 2020, 3–10. DOI:10.1007/978-981-32-9244-4_1.
  22. [22] Y. Liu, L. Dong, Y. Ji, and W. Xu, (2019) “Infrared and visible image fusion through details preservation" Sensors (Switzerland) 19(20): DOI: 10.3390/s19204556.
  23. [23] C. Gao, F. Liu, and H. Yan, (2020) “Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation" Journal of Intelligent and Fuzzy Systems 39(3): 4617–4629. DOI: 10.3233/JIFS-200554.


Xigui Zheng  1,2

1School of Mechanical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450000,China
2Henan Digital Intelligent Equipment Engineering Research Center


 

Received: December 12, 2020
Accepted: May 17, 2021
Publication Date: June 24, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202112_24(6).0017  


ABSTRACT


In the traditional infrared and visible image fusion algorithms, there are some problems such as week target, missing background, blur edge information and so on. Therefore, we propose a new infrared and visible image fusion algorithm based on a dual-channel spiking cortical model (DCSCM) via LatLRR in this paper. First, a non-down-sampling shearlet transform (NSST) is applied to the source image to ob-tain the corresponding low-frequency and high-frequency components. Then, the LatLRR method and DCSCM model are used to fuse the low and high frequency component, respectively. Finally, NSST in-verse transformation is performed for the fused components to obtain the final fused image. Compared with other state-of-the-art methods, the experiment results on different scenarios show that the pro-posed algorithm has better visual effect, and higher objective evaluation values. And it can play an im-portant role in Micro CNC engraving milling machine intelligent knife setting.


Keywords: Infrared and visible image fusion, dual-channel spiking cortical model, LatLRR, NSST, machine intelli-gent


REFERENCES


  1. [1] S. Yin and Y. Zhang. Singular value decomposition-based anisotropic diffusion for fusion of infrared and visible images. 2019. DOI: 10.1080/19479832.2018.1487886.
  2. [2] R. Olmos, S. Tabik, A. Lamas, F. Pérez-Hernández, and F. Herrera, (2019) “A binocular image fusion approach for minimizing false positives in handgun detection with deep learning" Information Fusion 49: 271–280. DOI: 10.1016/j.inffus.2018.11.015.
  3. [3] J. Reena Benjamin and T. Jayasree, (2018) “Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms" International Journal of Computer Assisted Radiology and Surgery 13(2): 229–240. DOI: 10.1007/s11548-017-1692-4.
  4. [4] S. Yin, J. Liu, and H. Li, (2018) “A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery" 3D Research 9(4): DOI: 10.1007/s13319- 018-0204-9.
  5. [5] L. Yang, B. L. Guo, andW. Ni, (2008) “Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform" Neurocomputing 72(1-3): 203–211. DOI: 10.1016/j.neucom.2008.02.025.
  6. [6] Q. Xing, D. Wang, and K. Bi, (2014) “Fusion technique for images based on non-subsampled contourlet transform and compressive sensing" Kongzhi yu Juece/Control and Decision 29(4): 585–592.
  7. [7] J. Adu, M. Wang, Z. Wu, and Z. Zhou, (2012) “Multifocus image fusion based on the non-subsampled contourlet transform" Journal of Modern Optics 59(15):1355–1362. DOI: 10.1080/09500340.2012.714802.
  8. [8] Y. Wu and Z. Wang, (2017) “Infrared and Visible Image Fusion Based on Target Extraction and Guided Filtering Enhancement [J]" Acta Optica Sinica 37(8): 91–101. DOI: 10.3788/AOS201737.0810001.
  9. [9] J. Liu, Y. J. Lei, and Y. Q. Xing, (2017) “Innovative image fusion method based on improved NSST" Control and Decision 32(2): 275–280. DOI: 10.13195/j.kzyjc. 2016.0075.
  10. [10] M.-l. Li, Y.-J. Li, H. Wang, and K. Zhang, (2010) “Fusion algorithm of infrared and visible images based on NSCT and PCNN" Opto-Electronic Engineering 37(6): 90–95. DOI: 10.3969/j.issn.1003-501X.2010.06.016.
  11. [11] Z. Song, H. Jiang, and S. Li, (2017) “A novel fusion framework based on adaptive PCNN in NSCT domain for whole-body PET and CT images" Computational and Mathematical Methods in Medicine 2017: DOI: 10.1155/2017/8407019.
  12. [12] N.Wang, Y. Ma, W. Wang, and S. Zhou, (2014) “An image fusion method based on NSCT and dual-channel PCNN model" Journal of Networks 9(2): 501–506.DOI: 10.4304/jnw.9.2.501-506.
  13. [13] H. Tao and X. Lu. “Smoke vehicle detection based on multi-feature fusion and hidden Markov model”. In: Journal of Real-Time Image Processing. 17. 3. Springer, 2020, 745–758. DOI: 10.1007/s11554-019-00856-z.
  14. [14] W. Kong, B. Wang, and Y. Lei, (2015) “Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model" Infrared Physics and Technology 71: 87–98. DOI: 10.1016/j. infrared.2015.02.008.
  15. [15] L. Junwu, B. Li, and Y. Jiang, (2020) “An Infrared and Visible Image Fusion Algorithm Based on LSWTNSST" IEEE Access 8: 179857–179880. DOI: 10.1109/ ACCESS.2020.3028088.
  16. [16] C. Zhao, Y. Huang, and S. Qiu, (2019) “Infrared and visible image fusion algorithm based on saliency detection and adaptive double-channel spiking cortical model" Infrared Physics and Technology 102: DOI: 10.1016/j. infrared.2019.102976.
  17. [17] X. Xing, C. Liu, C. Luo, and T. Xu, (2020) “Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition" Eurasip Journal on Wireless Communications and Networking 2020(1): DOI: 10. 1186/s13638-020-01774-6.
  18. [18] Z. Zhang, S. Yan, and M. Zhao. “Robust image representation and decomposition by Laplacian regularized latent low-rank representation”. In: Proceedings of the International Joint Conference on Neural Networks. 2013. DOI: 10.1109/IJCNN.2013.6707051.
  19. [19] H. Li and X. J. Wu. Infrared and visible image fusion using Latent Low-Rank Representation. 2018. arXiv: 1804.08992.
  20. [20] Z. Jiang, H.Wu, and X. Zhou, (2018) “Infrared and Visible Image Fusion Algorithm Based on Improved Guided Filtering and Dual-Channel Spiking Cortical Model" Guangxue Xuebao/Acta Optica Sinica 38(2): DOI: 10.3788/AOS201838.0210002.
  21. [21] Y. L. Qiao, X. Y. Gao, and C. Y. Song. “Near Infrared, Long-Wave Infrared and Visible Image Fusion Based on Oversampled Graph Filter Banks”. In: Lecture Notes in Electrical Engineering. 590. 2020, 3–10. DOI:10.1007/978-981-32-9244-4_1.
  22. [22] Y. Liu, L. Dong, Y. Ji, and W. Xu, (2019) “Infrared and visible image fusion through details preservation" Sensors (Switzerland) 19(20): DOI: 10.3390/s19204556.
  23. [23] C. Gao, F. Liu, and H. Yan, (2020) “Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation" Journal of Intelligent and Fuzzy Systems 39(3): 4617–4629. DOI: 10.3233/JIFS-200554.


    
 

0.7
2020CiteScore
 
 
33rd percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.