References
- O. Zelmati, B. Bondzulic, B. Pavlovic, I. Tot, and S. Merrouche, “Study of subjective and objective quality assessment of infrared compressed images”, Journal of Electrical Engineering, vol. 73, no. 2, pp. 73–87, doi: 10.2478/jee-2022-0011, 2022.
- D. Peric, B. Livada, M. Peric, and S. Vujic, “Thermal imager range: Predictions, expectations, and reality”, Sensors, vol. 19, no. 15, pp. 3313, doi: 10.3390/s19153313, 2019.
- R. Gade, and T. B. Moeslund, “Thermal cameras and applications: A survey”, Machine Vision and Applications, vol. 25, no. 1, pp. 245-262, doi: 10.1007/s00138-013-0570-5, 2014.
- D. L. Hickman, and S. J. Shepperd, “Image fusion systems for surveillance applications: Design options and constraints for a tri-band camera”, Proceedings SPIE Infrared Technology and Applications XLVII, pp. 310-328, doi: 10.1117/12.2584984, 2021.
- C. Jiang, H. Ren, H. Yang, H. Huo, P. Zhu, Z. Yao, J. Li, M. Sun, and S. Yang, “M2FNet: Multi-modal fusion network for object detection from visible and thermal infrared images”, International Journal of Applied Earth Observation and Geoinformation, vol. 130, pp. 103918, doi: 10.1016/j.jag.2024.103918, 2024.
- R. Li, M. Zhou, D. Zhang, Y. Yan, and Q. Huo, “A survey of multi-source image fusion”, Multimedia Tools and Applications, vol. 83, no. 6, pp. 18573-18605, doi: 10.1007/s11042-023-16071-9, 2024.
- Y. Hua, W. Xu, and Y. Ai, “A residual ConvNeXt-based network for visible and infrared image fusion”, 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI), Guangzhou, China, May 12-14, pp. 370-376, doi: 10.1109/ICECAI58670.2023.10176540, 2023.
- R. Soroush, Y. Baleghi, “NIR/RGB image fusion for scene classification using deep neural networks”, The Visual Computer, vol. 39, no. 7, pp. 2725-2739, doi: 10.1007/s00371-022-02488-0, 2023.
- X. Zhang, and Y. Demiris, “Visible and infrared image fusion using deep learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no 8, pp. 10535-10554, doi: 10.1109/TPAMI.2023.3261282, 2023.
- Y. Liu, S. Liu, and Z. Wang, “A general framework for image fusion based on multi-scale transform and sparse representation”, Information Fusion, vol. 24, pp. 147-164, doi: 10.1016/j.inffus.2014.09.004, 2015.
- H. Xu, J. Ma, J. Jiang, X. Guo, and H. Ling, “U2Fusion: A unified unsupervised image fusion network”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 502-518, doi: 10.1109/TPAMI.2020.3012548, 2022.
- J. Ma, L. Tang, F. Fan, J. Huang, X. Mei, and Y. Ma, “SwinFusion: Cross-domain long-range learning for general image fusion via Swin Transformer”, IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 7, pp. 1200-1217, doi 10.1109/JAS.2022.105686, 2022.
- A. Toet, M. A. Hogervorst, and A. R. Pinkus, “The TRICLOBS dynamic multi-band image data set for the development and evaluation of image fusion methods”, PLoS One, vol. 11, no. 12, pp. 0165016, doi: 10.1371/journal.pone.0165016, 2016.
- K. Xiao, X. Kang, H. Liu, and P. Duan, “MOFA: A novel dataset for multi-modal image fusion applications”, Information Fusion, vol. 96, pp. 144-155, doi: 10.1016/j.inffus.2023.03.012, 2023.
- Z. Liu, H. Mao, C.Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A ConvNet for the 2020s, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA, June 18-24, pp. 11966-11976, doi: 10.1109/CVPR52688.2022.01167, 2022.
- P. J. Burt, and E. H. Adelson, “The Laplacian pyramid as a compact image code”, IEEE Transactions on Communication, vol. 31, no. 4, pp. 532-540, doi: 10.1016/B978-0-08-051581-6.50065-9, 1983.
- X. Zhang, P. Ye, and G. Xiao, “VIFB: A visible and infrared image fusion benchmark”, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, June 14-19, pp. 468-478, doi: 10.1109/CVPRW50498.2020.00060, 2020.
- S. Singh, H. Singh, G. Bueno, O. Deniz, S Singh, H. Monga, P. N. Hrisheekesha, and A. Pedraza, “A review of image fusion: Methods, applications and performance metrics”, Digital Signal Processing, vol. 137, pp. 104020, doi: 10.1016/j.dsp.2023.104020, 2023.
- A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a ‘completely blind’ image quality analyzer”, IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209-212, doi: 10.1109/LSP.2012.2227726, 2013.
- A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain”, IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695-4708, doi: 10.1109/TIP.2012.2214050, 2012.
- N. Venkatanath, D. Praneeth, M. C. Bh, S. S. Channappayya, and S. S. Medasani, “Blind image quality evaluation using perception based features”, Twenty First National Conference on Communications (NCC), Mumbai, India, 27 February - 01 March, pp. 1-6, doi: 10.1109/NCC.2015.7084843, 2015.
- Z. M. Laidouni, B. Bondžulić, D. Bujaković, V. Petrović, T. Adli, and M. Andrić, “BTIFD: Bimodal and trimodal image fusion database”, Mendeley Data, V1, available at: https://data.mendeley.com/datasets/btnws5tbcm/1, 2024.