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Infrared and Visible Image Fusion Using Morphological Reconstruction Filters and Refined Toggle-Contrast Edge Features

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

In this paper, the authors have discussed a simple scheme for infrared-visible image fusion to combine the complementary information captured using multiple sensors with different properties. It employs a pair of morphological connected filters, i.e., opening and closing by reconstruction applied at each scale, brings out categorical bright and dark features from the source images. At each specific scale, the bright (or dark) features at a given spatial location from the constituting images are mutually compared in terms of clarity or prominence. Additionally, the cumulative gradient information is extracted using multiscale toggle-contrast operator which is further refined using a guided filter with respect to the source images. The final fusion is achieved by combining the best bright (or dark) features along with the refined edge information onto a base image. Experiments are conducted on pre-registered infrared-visible image pairs from the TNO IR-visible image dataset along with subjective and objective performance evaluation with due comparison against other state-of-the-art methods. The proposed method yields realistic fusion outputs and exhibits appreciable levels of performance with a better representation of complementary information which could facilitate decision-making in higher levels of image processing tasks.

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References

  1. Blum, R.S., Liu, Z.: Multi-sensor Image Fusion and Its Applications. CRC Press (2018)

    Google Scholar 

  2. Cai, H., Zhuo, L., Chen, X., Zhang, W.: Infrared and visible image fusion based on bemsd and improved fuzzy set. Infrared Phys. Technol. 98, 201–211 (2019)

    Article  Google Scholar 

  3. Chen, J., Li, X., Luo, L., Mei, X., Ma, J.: Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Inform. Sci. 508, 64–78 (2020)

    Article  Google Scholar 

  4. Ciprián-Sánchez, J.F., Ochoa-Ruiz, G., Gonzalez-Mendoza, M., Rossi, L.: Fire-gan: a novel deep learning-based infrared-visible fusion method for wildfire imagery. Neural Comput. Appl. 1–13 (2021)

    Google Scholar 

  5. Dogra, A., Goyal, B., Agrawal, S.: From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications. IEEE Access 5, 16040–16067 (2017)

    Article  Google Scholar 

  6. Guo, Z., Yu, X., Du, Q.: Infrared and visible image fusion based on saliency and fast guided filtering. Infrared Phys. Technol. 104178 (2022)

    Google Scholar 

  7. Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Comput. Electr. Eng. 37(5), 744–756 (2011)

    Article  MATH  Google Scholar 

  8. Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Inform. Fusion 14(2), 127–135 (2013)

    Article  Google Scholar 

  9. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intel. 35(6), 1397–1409 (2012)

    Article  Google Scholar 

  10. Jin, X., Jiang, Q., Yao, S., Zhou, D., Nie, R., Hai, J., He, K.: A survey of infrared and visual image fusion methods. Infrared Phys. Technol. 85, 478–501 (2017)

    Article  Google Scholar 

  11. Li, H., Wu, X.J., Kittler, J.: Infrared and visible image fusion using a deep learning framework. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2705–2710. IEEE (2018)

    Google Scholar 

  12. Li, S., Kang, X., Fang, L., Hu, J., Yin, H.: Pixel-level image fusion: a survey of the state of the art. Inform. Fusion 33, 100–112 (2017)

    Article  Google Scholar 

  13. Li, S., Yang, B., Hu, J.: Performance comparison of different multi-resolution transforms for image fusion. Inform. Fusion 12(2), 74–84 (2011)

    Article  Google Scholar 

  14. Li, W., Xie, Y., Zhou, H., Han, Y., Zhan, K.: Structure-aware image fusion. Optik 172, 1–11 (2018)

    Article  Google Scholar 

  15. Lin, Y., Cao, D., et al.: Adaptive Infrared and Visible Image Fusion Method by Using Rolling Guidance Filter and Saliency Detection. Optik, p. 169218 (2022)

    Google Scholar 

  16. Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inform. Fusion 45, 153–178 (2019)

    Article  Google Scholar 

  17. Meyer, F., Serra, J.: Contrasts and activity lattice. Signal Proces. 16(4), 303–317 (1989)

    Google Scholar 

  18. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind’’ image quality analyzer. IEEE Signal Proces. Lett. 20(3), 209–212 (2012)

    Article  Google Scholar 

  19. Patel, A., Chaudhary, J.: A review on infrared and visible image fusion techniques. In: Intelligent Communication Technologies and Virtual Mobile Networks, pp. 127–144. Springer (2019)

    Google Scholar 

  20. Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429), Vol. 3, pp. III–173. IEEE (2003)

    Google Scholar 

  21. Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Trans. Image Proces. 4(8), 1153–1160 (1995)

    Article  Google Scholar 

  22. Toet, A.: The tno multiband image data collection. Data Brief 15, 249 (2017)

    Article  Google Scholar 

  23. Wang, B., Zou, Y., Zhang, L., Li, Y., Chen, Q., Zuo, C.: Multimodal super-resolution reconstruction of infrared and visible images via deep learning. Opt. Lasers Eng. 156, 107078 (2022)

    Article  Google Scholar 

  24. Xu, H., Ma, J., Jiang, J., Guo, X., Ling, H.: U2fusion: a unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 502–518 (2020)

    Article  Google Scholar 

  25. Zhan, K., Kong, L., Liu, B., He, Y.: Multimodal image seamless fusion. J. Electron. Imaging 28(2), 023027 (2019)

    Article  Google Scholar 

  26. Zhan, K., Xie, Y., Wang, H., Min, Y.: Fast filtering image fusion. J. Electron. Imaging 26(6), 063004 (2017)

    Article  Google Scholar 

  27. Zhang, H., Xu, H., Xiao, Y., Guo, X., Ma, J.: Rethinking the image fusion: a fast unified image fusion network based on proportional maintenance of gradient and intensity. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12797–12804 (2020)

    Google Scholar 

  28. Zhang, Y., Liu, Y., Sun, P., Yan, H., Zhao, X., Zhang, L.: Ifcnn: a general image fusion framework based on convolutional neural network. Inform. Fusion 54, 99–118 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology (Indian School of Mines) Dhanbad, Jharkhand, 826004, India

    Manali Roy & Susanta Mukhopadhyay

Authors
  1. Manali Roy
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  2. Susanta Mukhopadhyay
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Corresponding author

Correspondence to Manali Roy .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Roy, M., Mukhopadhyay, S. (2023). Infrared and Visible Image Fusion Using Morphological Reconstruction Filters and Refined Toggle-Contrast Edge Features. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_51

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