DataVXJournal of Bionic EngineeringPaper
journal article·2024-05

Image Fusion Based on Bioinspired Rattlesnake Visual Mechanism Under Lighting Environments of Day and Night Two Levels

Yong Wang, Hongmin Zou

Journal of Bionic Engineering· Volume 21· Issue 3
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Abstract

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OpenAlex topics: Advanced Image Fusion Techniques · Visual Attention and Saliency Detection · Remote-Sensing Image Classification

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References (39)

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1. Terashima, S., & Ogawa, K. (2002). Degeneration of infrared receptor terminals of snakes caused by capsaicin. Brain Research, 958, 468–471. https://doi.org/10.1016/S0006-8993(02)03746-010.1016/S0006-8993(02)03746-0 ↗
2. Tang, D., Xiong, Q. Y., Yin, H. P., Zhu, Z. Q., & Li, Y. X. (2022). A novel sparse representation based fusion approach for multi-focus images. Expert Systems with Applications, 197, 116737. https://doi.org/10.1016/j.eswa.2022.11673710.1016/j.eswa.2022.116737 ↗
3. Zhang, H., Han, X. N., & Zhang, R. (2022). Multimodal image fusion method based on guided filter. International Journal of Pattern Recognition and Artificial Intelligence, 36, 1–16. https://doi.org/10.1142/S021800142254003910.1142/S0218001422540039 ↗
4. Wan, H., Tang, X. L., Zhu, Z. Q., & Li, W. S. (2021). Multi-focus image fusion method based on multi-Scale decomposition of information complementary. Entropy, 23(10), 1362. https://doi.org/10.3390/e2310136210.3390/e23101362 ↗
5. Hu, Y. X., Chen, Z. J., Zhang, B., Ma, L. F., & Li, J. Q. (2021). A multi-focus image fusion method based on multi-source joint layering and convolutional sparse representation. IET Image Processing, 16, 216–228. https://doi.org/10.1049/ipr2.1234510.1049/ipr2.12345 ↗
6. Li, S. T., Kang, X. D., & Hu, J. W. (2013). Image fusion with guided filtering. IEEE Transactions on Image Processing, 22, 2864–2875. https://doi.org/10.1109/TIP.2013.224422210.1109/TIP.2013.2244222 ↗
7. Bavirisetti, D. P., Xiao, G., & Liu, G. (2017). Multi-sensor image fusion based on fourth order partial differential equations. In 20th International conference on information fusion (pp. 701–709). Xi’an, China. https://doi.org/10.23919/ICIF.2017.800971910.23919/ICIF.2017.8009719 ↗
8. Li, Q. Q., Han, G. L., Liu, P. X., Yang, H., Wu, J. J., & Liu, D. X. (2021). An infrared and visible image fusion method guided by saliency and gradient information. IEEE Access, 9, 108942–108958. https://doi.org/10.1109/ACCESS.2021.310163910.1109/ACCESS.2021.3101639 ↗
9. Fan, W. Q., Li, X. Y., & Liu, Z. C. (2023). Fusion of visible and infrared images using GE-WA model and VGG-19 network. Scientific Reports, 13, 190. https://doi.org/10.1038/s41598-023-27391-z10.1038/s41598-023-27391-z ↗
10. Ciprián-Sánchez, J. F., Ochoa-Ruiz, G., Gonzalez-Mendoza, M., & Rossi, L. (2021). FIRe-GAN: A novel deep learning-based infrared-visible fusion method for wildfire imagery. Neural Computing and Applications, 35, 1–13. https://doi.org/10.1007/S00521-021-06691-310.1007/S00521-021-06691-3 ↗
11. Wu, C. M., & Chen, L. (2020). Infrared and visible image fusion method of dual NSCT and PCNN. PLoS ONE, 15, e0239535. https://doi.org/10.1371/journal.pone.023953510.1371/journal.pone.0239535 ↗
12. Bavirisetti, D. P., & Dhuli, R. (2015). Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sensors. Journal, 16, 203–209. https://doi.org/10.1109/JSEN.2015.247865510.1109/JSEN.2015.2478655 ↗
13. Pang, S., Huo, H. T., Yang, X., Li, J., & Liu, X. W. (2023). Infrared and visible image fusion based on double fluid pyramids and multi-scale gradient residual block. Infrared Physics and Technology, 131, 104702. https://doi.org/10.1016/j.infrared.2023.10470210.1016/j.infrared.2023.104702 ↗
14. Fang, A. Q., Wu, J. S., & Li, Y. (2023). Infrared and visible image fusion via mutual information maximization. Computer Vision and Image Understanding, 231, 103683. https://doi.org/10.1016/j.cviu.2023.10368310.1016/j.cviu.2023.103683 ↗
15. Schraft, H. A., Bakken, G. S., & Clark, R. W. (2019). Infrared-sensing snakes select ambush orientation based on thermal backgrounds. Scientific Reports, 9, 3950. https://doi.org/10.1038/s41598-019-40466-010.1038/s41598-019-40466-0 ↗
16. Chen, Q., Liu, Y., Brauth, S. E., Fang, G., & Tang, Y. (2017). The thermal background determines how the infrared and visual systems interact in pit vipers. Journal of Experimental Biology, 220, 3103–3109. https://doi.org/10.1242/jeb.15538210.1242/jeb.155382 ↗
17. Gower, D. J., Sampaio, F. L., Peichl, L., Wagner, H. J., Loew, E. R., Mclamb, W., Douglas, R. H., Orlov, N., Grace, M., Hart, N. S., Hunt, D. M., Partridge, J. C., & Simões, B. F. (2019). Evolution of the eyes of vipers with and without infrared-sensing pit organs. Biological Journal of the Linnean Society, 126, 796–823. https://doi.org/10.1093/biolinnean/blz00310.1093/biolinnean/blz003 ↗
18. Darbaniyan, F., Mozafari, K., Liu, L., & Sharma, P. (2021). Soft matter mechanics and the mechanisms underpinning the infrared vision of snakes. Matter, 4, 241–252. https://doi.org/10.1016/j.matt.2020.09.02310.1016/j.matt.2020.09.023 ↗
19. Kuffler, S. W. (1953). Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology, 16, 37–68. https://doi.org/10.1152/jn.1953.16.1.3710.1152/jn.1953.16.1.37 ↗
20. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117, 500–544. https://doi.org/10.1113/jphysiol.1952.sp00476410.1113/jphysiol.1952.sp004764 ↗
21. Hartline, P. H., Kass, L., & Loop, M. S. (1978). Merging of modalities in the optic tectum: Infrared and visual integration in rattlesnakes. Science, 199, 1225–1229. https://doi.org/10.1126/science.62883910.1126/science.628839 ↗
22. Newman, E. A., & Hartline, P. H. (1981). Integration of visual and infrared information in bimodal neurons in the rattlesnake optic tectum. Science, 213, 789–791. https://doi.org/10.1126/science.725628110.1126/science.7256281 ↗
23. Waxman, A. M., Gove, A. N., Fay, D. A., Racamato, J. P., Carrick, J. E., Seibert, M. C., & Savoye, E. D. (1997). Color night vision: Opponent processing in the fusion of visible and IR imagery. Neural Networks, 10, 1–6. https://doi.org/10.1016/s0893-6080(96)00057-310.1016/s0893-6080(96)00057-3 ↗
24. Zhang, Z., Li, H., & Zhao, G. R. (2018). Bionic algorithm for color fusion of infrared and low light level image based on rattlesnake bimodal cells. IEEE Access, 6, 68981–68988. https://doi.org/10.1109/ACCESS.2018.288084510.1109/ACCESS.2018.2880845 ↗
25. Li, H. (2016). A Research on Image Enhancement Algorithm Based on the Fusion Mechanism of Biological Vision. Master's thesis, University of Electronic Science and Technology of China. (in Chinese)
26. Wang, Y., Wang, Y. Q., & Ma, J. (2016). Improved rattle snake dual mode cell model for image fusion. Optics and Precision Engineering, 24, 2848–2854. https://doi.org/10.3788/OPE.20162411.2848. in Chinese.10.3788/OPE.20162411.2848 ↗
27. Jing, S. W. (2009). Study of Bionic False Color Fusion of Multi-band Images. Master's thesis, North University of China. (in Chinese)
28. Wang, Y., Liu, H. Q., Xie, W. J., & Wang, S. G. (2022). Image fusion based on the rattlesnake visual receptive field model. Displays, 74, 102171. https://doi.org/10.1016/j.displa.2022.10217110.1016/j.displa.2022.102171 ↗
29. Wang, Y., Liu, H. Q., & Wang, X. G. (2022). Pseudo color fusion of infrared and visible images based on the rattlesnake vision imaging system. Journal Bionic Engineering, 19, 209–223. https://doi.org/10.1007/s42235-021-00127-310.1007/s42235-021-00127-3 ↗
30. Zhu, H. R., Liu, Y. Q., & Zhang, W. Y. (2019). Night-vision image fusion based on intensity transformation and two-scale decomposition. Journal of Electronics & Information Technology, 41, 640–648. https://doi.org/10.11999/JEIT18040710.11999/JEIT180407 ↗
31. Wu, C., Zhan, J. H., & Jin, J. C. (2018). Nighttime images fusion based on Laplacian pyramid. Proceedings of the SPIE, 10607, 106070X. https://doi.org/10.1117/12.230583510.1117/12.2305835 ↗
32. Hogervorst, A. M., & Toet, A. (2017). Improved color mapping methods for multiband nighttime image fusion. Journal of Imaging, 3(3), 1–25. https://doi.org/10.3390/jimaging303003610.3390/jimaging3030036 ↗
33. Bhatnagar, G., & Liu, Z. (2015). A novel image fusion framework for night-vision navigation and surveillance. Signal Image and Video Processing, 9(1s), 165–175. https://doi.org/10.1007/s11760-014-0740-610.1007/s11760-014-0740-6 ↗
34. Chen, Q., Deng, H., Brauth, S. E., Ding, L., & Tang, Y. (2012). Reduced performance of prey targeting in pit vipers with contralaterally occluded infrared and visual senses. PLoS ONE, 7, 1–8. https://doi.org/10.1371/journal.pone.003498910.1371/journal.pone.0034989 ↗
35. Shou, T. D. (2010). Brain Mechanisms of Visual Information Processing (2nd ed.). University of Science and Technology of China Press. (in Chinese)
36. Land, E. H., & McCann, J. J. (1971). Lightness and retinex theory. Journal of Optical Society of America, 61, 1–11. https://doi.org/10.1364/JOSA.61.00000110.1364/JOSA.61.000001 ↗
37. Grossberg, S., Mingolla, E., & Williamson, J. (1995). Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation. Neural Networks, 8, 1005–1028. https://doi.org/10.1016/0893-6080(95)00079-810.1016/0893-6080(95)00079-8 ↗
38. Newman, E. A., & Hartline, P. H. (1982). The infrared vision of snakes. Scientific American, 246, 116–127. https://doi.org/10.1038/scientificamerican0382-11610.1038/scientificamerican0382-116 ↗
39. Wang, Y., & Liu, H. Q. (2020). Pseudo color image fusion based on rattlesnake’s visual receptive field model. In 2020 IEEE international conference on artificial intelligence and information systems (ICAIIS) (pp. 596–600). Dalian, China.https://doi.org/10.1109/ICAIIS49377.2020.919479810.1109/ICAIIS49377.2020.9194798 ↗