Abstract
Image fusion aims at the integration of different complementary image data into a distinct, new image with the best achievable quality. Fusion of visible and infrared images provides complementary performance which is frequently required in many standard vision-based systems. For example, military and surveillance systems require target detection (thermal) followed by identification (visible); Comparative analysis of different fusion techniques with Latent low rank method (LLR) is done on different military and surveillance applications. In case of concealed weapon detection, LLR performance is good, where as DWT based fusion techniques are suitable for surveillance applications but in case of certain data sets feature extraction is not appropriate. In this paper, Latent low rank method, which is an accurate technique for Image fusion to find hidden weapons or other objects hidden beneath an individual’s clothing, is presented. LLR technique is implemented using MATLAB-2019 tool. Latent low rank representation has the power to spot salient features. This particular model de-noises and decomposes the image simultaneously. This method is simple and effective. The percentage of detection of objects is 94.6%. Different metrics are used for evaluating fusion performance subjectively. Simulation results and subjective evaluation shows that LLR is more suitable for concealed weapon detection application.
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References
Ben Hamza, A., Yun, H., Hamid, K., & Alan, W. (2005). A multiscale approach to pixel-level image fusion. Integrated Computer-Aided Engineering, 12(2), 135–146.
Du, J., Li, W., Ke, L., & Xiao, B. (2016). An overview of multi-modal medical image fusion. Neurocomputing, 215, 3–20.
Han, X., Lv, T., Song, X., Nie, T., Liang, H., He, B., & Kuijper, A. (2019). An adaptive two-scale image fusion of visible and infrared images. IEEE Access, 7, 56341–56352.
He, K., Sun, J., & Tang, X. (2013). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397–1409.
Jyothi, G. N., Anusha, G., & Thirumalesu, K. (2020). Asic implementation of linear equalizer using adaptive fir filter. International Journal of e-Collaboration (IJeC), 16(4), 59–71.
Li, S., Kang, X., Fang, L., Jianwen, H., & Yin, H. (2017). Pixel-level image fusion: A survey of the state of the art. Information Fusion, 33, 100–112.
Li, H., Manjunath, B. S., & Mitra, S. K. (1995). Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57(3), 235–245.
Mumtaz, A., Abdul M., & Adeel M. (2008). Genetic algorithms and its application to image fusion. In 2008 4th International Conference on Emerging Technologies, pp. 6–10. IEEE.
NagaJyothi, G., & Sriadibhatla S. (2017). Distributed arithmetic architectures for fir filters-a comparative review. In 2017 International conference on wireless communications, signal processing and networking (WiSPNET), pp. 2684–2690. IEEE.
NagaJyothi, G., & Sridevi, S. (2019). High speed and low area decision feed-back equalizer with novel memory less distributed arithmetic filter. Multimedia Tools and Applications, 78, 32679–32693.
Uner, M. K., Liane C. R., Pramod K. V., Mark G. A. (1997). Concealed weapon detection: an image fusion approach. In Investigative image processing (Vol. 2942, pp. 123–132). International Society for Optics and Photonics.
Zhang, P., Yuan, Y., Fei, C., Tian, P., & Wang, S. (2018). Infrared and visible image fusion using co-occurrence filter. Infrared Physics and Technology, 93, 223–231.
Zhang, Y., Zhang, L., Bai, X., & Zhang, L. (2017). Infrared and visual image fusion through infrared feature extraction and visual information preservation. Infrared Physics and Technology, 83, 227–237.
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Bhavana, D., Kishore Kumar, K. & Ravi Tej, D. Infrared and visible image fusion using latent low rank technique for surveillance applications. Int J Speech Technol 25, 551–560 (2022). https://doi.org/10.1007/s10772-021-09822-2
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DOI: https://doi.org/10.1007/s10772-021-09822-2