Abstract
Image fusion plays a vital role in many fields. Especially, fusion of infrared and visible images has high importance in every scenario from computer vision to medical sector. The objective of this work is to develop an effective method for producing clear objects with high spatial resolution along with background information by fusing infrared (IR) and visible (VIS) images. This integrated image can be efficiently utilized by humans or machines. To achieve this objective, we propose the use of Multi-Layer Bilateral Filtering (BF) and Gaussian Filtering (GF) techniques, which improvises the skewness and kurtosis of fused images. While the BF technique consistently produces higher quality images, the GF approach outperforms it by 86% in terms of statistical measures such as skewness and kurtosis. The findings demonstrate that the GF technique yields outputs with reduced noise and improved visual appeal. In this paper, we compare the assessment metrics of several outputs for both single images and a set of 100 images.
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References
Zhou Z, Wang B, Li S, Dong M (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters. Inf Fusion 30:15–26
Ma J, Zhou Z, Wang B, Hua Z (2017) Infrared and visible image fusion based on visual saliency map and weighted least square optimization. Infrared Phys Technol 82:8–17
Li S, Kang X, Fang L, Hu J, Yin H (2017) Pixel-level image fusion: a survey of the state of the art. Inf Fusion 33:100–112
Kumar P, Mittal A, Kumar P (2006) Fusion of thermal infrared and visible spectrum video for robust surveillance. In: Indian conference on computer vision, graphics and image processing, pp 528–539
Guan D, Cao Y, Yang J, Cao Y, Tisse C (2018) Exploiting fusion architectures for multispectral pedestrian detection and segmentation. Appl Opt 57(18)(D):108–116
Qian X, Han L, Cheng Y (2014) An object tracking method based on local matting for night fusion image. Infrared Phys Technol 67:455–461
Li J, Peng Y, Jiang T (2021) Embedded real-time infrared and visible image fusion for UAV surveillance. J Real-Time Image Process 18:2331–2345
Li H, Wu X-J, Kittler J (2020) MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Trans Image Process 29:4733–4746
Liu X, Mei W, Du H (2017) Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion. Neurocomputing 235:131–139
Zhu HR, Liu YQ, Zhang WY (2019) Infrared and visible image fusion based on iterative guided filtering and multi-visual weight information. Acta Photonica Sinica 48(3):0310002
Karim S, Tong G, Li J, Qadir A, Farooq U, Yiting Y (2023) Current advances and future perspectives of image fusion: a comprehensive review. Inf Fusion 90:185–217
Tan W, Zhou H et al (2019) Infrared and visible image perceptive fusion through multi-level Gaussian curvature filtering image decomposition. Appl Opt 58:3064–3073
Ma J, Qiu W, Zhao J, Ma Y, Yuille A, Tu Z (2015) Robust L2E estimation of transformation for non-rigid registration. IEEE Trans Signal Process 63:1115–1129
Wang C, Yang G, Sun D, Zuo J, Li Z, Ma X (2021) A novel lightweight infrared and visible image fusion algorithm. In: 2021 international conference of optical imaging and measurement (ICOIM), 978-1-6654-0354-2/21. https://doi.org/10.1109/ICOIM52180.2021.9524368
Hu Y, He J, Xu L (2021) Infrared and visible image fusion based on multiscale decomposition with Gaussian and co-occurrence filters. In: 4th international conference on pattern recognition and artificial intelligence (PRAI), Yibin, China, pp 46–50. https://doi.org/10.1109/PRAI53619.2021.9551089
Zhang MM, Choi J, Daniilidis K, Wolf MT, Kanan C (2015) VAIS: a dataset for recognizing maritime imagery in the visible and infrared spectrums [IEEE OTCBVS WS series bench]. In: Proceedings of the 11th IEEE workshop on perception beyond the visible spectrum (PBVS-2015)
Acknowledgements
The authors acknowledge sources of the datasets used for testing the image processing techniques in this research paper. First two authors (Kesari Eswar Bhageerath (KEB) and Ashapurna Marandi) express their sincere thanks to Head CSIR Fourth Paradigm Institute (CSIR-4PI) to carry out this research work, also KEB wish to thank Dr Ashapurna Marandi to carry out internship under her guidance in CSIR-4PI. He also expresses his sincere gratitude and appreciation to the Head and the faculty in the Computer Science Dept of Gayatri Vidya Parishad College of Engineering for their encouragement and guidance.
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Bhageerath, K.E., Marndi, A., Harini, D.N.D. (2024). Performance Assessment of Gaussian Filter-Based Image Fusion Algorithm. In: Kumar, S., K., B., Kim, J.H., Bansal, J.C. (eds) Fourth Congress on Intelligent Systems. CIS 2023. Lecture Notes in Networks and Systems, vol 868. Springer, Singapore. https://doi.org/10.1007/978-981-99-9037-5_4
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DOI: https://doi.org/10.1007/978-981-99-9037-5_4
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